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Journal articles on the topic 'Relation extractor'
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Yuan, Yujin, Liyuan Liu, Siliang Tang, Zhongfei Zhang, Yueting Zhuang, Shiliang Pu, Fei Wu, and Xiang Ren. "Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 419–26. http://dx.doi.org/10.1609/aaai.v33i01.3301419.
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Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.
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Kim, Kuekyeng, Yuna Hur, Gyeongmin Kim, and Heuiseok Lim. "GREG: A Global Level Relation Extraction with Knowledge Graph Embedding." Applied Sciences 10, no. 3 (February 10, 2020): 1181. http://dx.doi.org/10.3390/app10031181.
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In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.
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Oliveira Neto, Waldemar de, Antonio Saraiva Muniz, Maria Anita Gonçalves da Silva, Cesar de Castro, and Clovis Manuel Borkert. "Boron extraction and vertical mobility in Paraná State oxisol, Brazil." Revista Brasileira de Ciência do Solo 33, no. 5 (October 2009): 1259–67. http://dx.doi.org/10.1590/s0100-06832009000500019.
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The deficiency or excess of micronutrients has been determined by analyses of soil and plant tissue. In Brazil, the lack of studies that would define and standardize extraction and determination methods, as well as lack of correlation and calibration studies, makes it difficult to establish limits of concentration classes for analysis interpretation and fertilizer recommendations for crops. A specific extractor for soil analysis is sometimes chosen due to the ease of use in the laboratory and not in view of its efficiency in determining a bioavailable nutrient. The objectives of this study were to: (a) evaluate B concentrations in the soil as related to the fertilizer rate, soil depth and extractor; (b) verify the nutrient movement in the soil profile; (c) evaluate efficiency of Hot Water, Mehlich-1 and Mehlich-3 as available B extractors, using sunflower as test plant. The experimental design consisted of complete randomized blocks with four replications and treatments of five B rates (0, 2, 4, 6, and 8 kg ha-1) applied to the soil surface and evaluated at six depths (0-0.05, 0.05-0.10, 0.10-0.15, 0.15-0.20, 0.20-0.30, and 0.30-0.40 m). Boron concentrations in the soil extracted by Hot Water, Mehlich-1 and Mehlich-3 extractors increased linearly in relation to B rates at all depths evaluated, indicating B mobility in the profile. The extractors had different B extraction capacities, but were all efficient to evaluate bioavailability of the nutrient to sunflower. Mehlich-1 and Mehlich-3 can therefore be used to analyze B as well as Hot Water.
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Zhang, Congle, Stephen Soderland, and Daniel S. Weld. "Exploiting Parallel News Streams for Unsupervised Event Extraction." Transactions of the Association for Computational Linguistics 3 (December 2015): 117–29. http://dx.doi.org/10.1162/tacl_a_00127.
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Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations ( e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open challenge. This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.
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Li, Bo, Jiyu Wei, Yang Liu, Yuze Chen, Xi Fang, and Bin Jiang. "Few-Shot Relation Extraction on Ancient Chinese Documents." Applied Sciences 11, no. 24 (December 17, 2021): 12060. http://dx.doi.org/10.3390/app112412060.
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Traditional humanity scholars’ inefficient method of utilizing numerous unstructured data has hampered studies on ancient Chinese writings for several years. In this work, we aim to develop a relation extractor for ancient Chinese documents to automatically extract the relations by using unstructured data. To achieve this goal, we proposed a tiny ancient Chinese document relation classification (TinyACD-RC) dataset annotated by historians and contains 32 types of general relations in ShihChi (a famous Chinese history book). We also explored several methods and proposed a novel model that works well on sufficient and insufficient data scenarios, the proposed sentence encoder can simultaneously capture local and global features for a certain period. The paired attention network enhances and extracts relations between support and query instances. Experimental results show that our model achieved promising performance with scarce corpus. We also examined our model on the FewRel dataset and found that outperformed the state-of-the-art no pretraining-based models by 2.27%.
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Toma, Claudia Crina, Teresa Casacchia, Claudia D`ippolito, and Giancarlo Statti. "Ficus carica SSP Dottato Buds by Intercropping Different Species: Metabolites, Antioxidant Activity and Endogenous Plant Hormones (IAA, ABA)." Revista de Chimie 68, no. 7 (August 15, 2017): 1628–31. http://dx.doi.org/10.37358/rc.17.7.5731.
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Ficus carica is one of the most common tree crops in the Mediterranean basin. Its ethnobotanic use has been extensively studied to evaluate its biological activity in relation to the presence of specific secondary metabolites. In this paper, the extract of the gemstones of the ficus carica ssp dottato di Cosenza was studied with respect to different vegetation habitats (intercrops) and two different extraction techniques. Buds, in fact, are used in gemotherapy as macerated glycerides obtained by long extraction processes (21 Days).The use of a Dynamic extractor (Naviglio� Extractor) has allowed not only to reduce the extraction time (10 h) but to obtain a qualitatively and quantitatively enriched extract withactive ingredients to which the specific biological activity is reported. In fact, the total polyphenolic and total flavonoid components were determined, of which Quercetin-3O-Glucoside and 3-O-Rhamnoside were dosed, and the resulting anti-oxidant activity. IAA and ABA have also been quantified.
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Marcheggiani, Diego, and Ivan Titov. "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations." Transactions of the Association for Computational Linguistics 4 (December 2016): 231–44. http://dx.doi.org/10.1162/tacl_a_00095.
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We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.
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Dowling, AJ, and CJ Howitt. "Effects of extraction technique on concentrations of soluble salts in soil saturation extracts." Soil Research 25, no. 2 (1987): 137. http://dx.doi.org/10.1071/sr9870137.
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Solution yield and saturation extract salinity for a range of south-east Queensland soils are described in relation to solution extraction by leaching, over a range of extraction times, and by centrifuging. Yield, pH, [Ca] and [HCO3] were affected by extraction technique. In leached extracts, compared with centrifuged extracts, solution pH was more variable and [HCO3] and [Ca] were higher. Leaching overnight consistently produced solution yields in excess of 20 g 100g-1 added water to saturation. Variations in these attributes reflect differences between the two extraction techniques which can be explained in terms of variable levels of CO2 in the solution collection assembly with air entry during extraction. Centrifuging minimised air entry and, hence, the gadliquid ratio in the extractor. The CO2 mediated changes are thus less for centrifuged than leached extracts. Centrifuging, as an extraction technique, therefore, has more relevance than leaching if specific mineralogic controls on solution composition are being determined.
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Wu, Ming-Jui, Wei-Ling Chen, Chung-Dann Kan, Fan-Ming Yu, Su-Chin Wang, Hsiu-Hui Lin, and Chia-Hung Lin. "Dysfunction Screening in Experimental Arteriovenous Grafts for Hemodialysis Using Fractional-Order Extractor and Color Relation Analysis." Cardiovascular Engineering and Technology 6, no. 4 (August 4, 2015): 463–73. http://dx.doi.org/10.1007/s13239-015-0239-5.
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OLALERE, OLUSEGUN ABAYOMI. "COMPARATIVE STUDY OF PULSED MICROWAVE AND HYDRODISTILLATION EXTRACTION OF PIPERINE OIL FROM BLACK PEPPER." IIUM Engineering Journal 18, no. 2 (December 1, 2017): 87–93. http://dx.doi.org/10.31436/iiumej.v18i2.802.
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Black pepper is a tropical crop with an extensive medicinal potential in alternative medicine and nutraceutical applications. The pungent bioactive piperine is responsible for this functions and its efficacy requires an efficient extraction technologies for optimal isolation. There is therefore a need to determine the best factor settings that will optimize the relative efficiency of the extraction system with minimal variability. The best factor settings was achieved using the L9 Taguchi parametric orthogonal design. The extraction parameters considered under this study were extraction time, irradiation power level, particle size and molar ratio. An optimal extraction condition was therefore achieved at 90 min extraction time, 350 W microwave power, 0.105 mm particle size and 10 mL/g molar ratio. The signal-to-noise ratio (SNR) otherwise known as response optimizer is an ideal metric in the determination of this optimum condition. The performance evaluation of reflux microwave extractor in relation to that of the hydrodistillation system placed the optimal efficiency and signal-to-noise ratio at 155.72% and 43.8469, respectively. The higher optimal efficiency obtained indicated that the microwave reflux extraction is better and more efficient than the conventional hydrodistillation techniques.
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Gallo, Monica, Andrea Formato, Gaetano Formato, and Daniele Naviglio. "Comparison between Two Solid-Liquid Extraction Methods for the Recovery of Steviol Glycosides from Dried Stevia Leaves Applying a Numerical Approach." Processes 6, no. 8 (July 30, 2018): 105. http://dx.doi.org/10.3390/pr6080105.
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Abstract: Stevia rebaudiana Bertoni is a perennial shrub belonging to the Asteraceae family. The leaves contain a mixture of steviol glycosides with extraordinary sweetening properties, among which the most important are stevioside and rebaudioside A. These components have a high sweetening power, which is about 300 times that of sucrose, and a negligible calorie content. However, their extraction and purification are not easy. In this paper, the extraction technique under cyclic pressure, known as rapid solid-liquid dynamic extraction (RSLDE), was compared using a Naviglio extractor (NE) with conventional maceration. The aim was to identify an efficient and economically viable method for obtaining high amounts of steviol glycosides in a short time. Furthermore, a numerical model was set up for the solid-liquid extraction process of value-added compounds from natural sources. Several parameters must be evaluated in relation to the characteristics of the parts of the plant subjected to extraction. Therefore, since diffusion and osmosis are highly dependent on temperature, it is necessary to control the temperature of the extraction system. On the other hand, the final aim of this work was to provide a scientific and quantitative basis for RSLDE. Therefore, the results obtained from stevia extracts using the corresponding mathematical model allowed hypothesizing the application of this model to the extraction processes of other vegetable matrices.
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Choi, Won-Hyuk, and Yong-Suk Choi. "Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning." Sensors 22, no. 9 (April 30, 2022): 3433. http://dx.doi.org/10.3390/s22093433.
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With the increase in the performance of deep learning models, the model parameter has increased exponentially. An increase in model parameters leads to an increase in computation and training time, i.e., an increase in training cost. To reduce the training cost, we propose Compositional Intelligence (CI). This is a reuse method that combines pre-trained models for different tasks. Since the CI uses a well-trained model, good performance and small training cost can be expected in the target task. We applied the CI to the Image Captioning task. Compared to using a trained feature extractor, the caption generator is usually trained from scratch. On the other hand, we pre-trained the Transformer model as a caption generator and applied CI, i.e., we used a pre-trained feature extractor and a pre-trained caption generator. To compare the training cost of the From Scratch model and the CI model, early stopping was applied during fine-tuning of the image captioning task. On the MS-COCO dataset, the vanilla image captioning model reduced training cost by 13.8% and improved performance by up to 3.2%, and the Object Relation Transformer model reduced training cost by 21.3%.
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Klepach, Halyna, Svitlana Voloshanska, Halyna Kovalchuk, and Aniela Stopa. "BIOLOGICALLY ACTIVE PROPERTIES OF THE ETHANOL AND AQUEOUS EXTRACTS FROM THE NEEDLES OF JUNIPERUS COMMUNIS." Scientific Journal of Polonia University 34, no. 3 (April 3, 2019): 104–12. http://dx.doi.org/10.23856/3413.
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The authors have tested different methods for preparing ethanol and aqueous extracts from the needles of the natural forms of the common juniper and researched some of their biologically active properties. They found 70% ethanol to be the best extractor of bioactive substances from the needles of the common juniper in contrast to 50% and 30% ethanol. They prove that the optimal way to obtain alcohol extracts is crushing the needles of the common juniper to linear sizes: 0.5-2 mm and to infuse in 70 % of ethanol for at least 20 days. Alcohol extracts obtained in this way (10%, g/g) contain polyphenols in the mass concentration of 0.40±0.02 mg/g biomass, ascorbic acid – 1.66±0.1 μg/g of mass and have weak antibacterial properties in relation to the microbiological test culture Escherihia coli, but not in relation to Streptococcus epidermis. A simple and at the same time optimal method to obtain aqueous extracts (10%, g/g) is boiling the uncrushed pieces of J. communis in water for 5 minutes. The aqueous extracts obtained by boiling of both crushed and uncrushed raw materials do not have an antibacterial action on the microbial test cultures.
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Aguiar, Camila Zacche de, Davidson Cury, and Amal Zouaq. "Minerando Mapa Conceitual a partir de Texto em Português." Revista Brasileira de Informática na Educação 27, no. 01 (January 1, 2019): 83. http://dx.doi.org/10.5753/rbie.2019.27.01.83.
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Concept maps are graphical tools for representation and construction of knowledge. The manual construction of a concept map requires time and cognitive effort, this being increased when the map should not represent the cognitive structure of the author, but rather, the information expressed in a text written by another author. Therefore, we propose a computational approach for concept map mining from texts in Portuguese that aims to represent the text in summary form through concepts and relationships. To this end, we define a technological architecture that includes the services of: (i) text formatting, removing characters and designing of the text; (ii) domain identification, information retrieval techniques to identify the domain to which refers the text; (iii) elements extractor, natural language processing techniques on the text to extract concept-relation-concept propositions; (iv) element summarizer, supported by graph analysis to identify the relevant concepts on the map; and (v) map visualization, presentation of the propositions in graphic form. The approach developed presents satisfactory results and contributes exceptionally to the summarization of texts to identify the relevant concepts of the text while maintaining its several and most important characteristics. Furthermore, this research introduces the specification of a project to provide computational resources for processing, handling and extraction of conceptual maps.
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Chen, Yile, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, and Fuzheng Zhang. "Points-of-interest relationship inference with spatial-enriched graph neural networks." Proceedings of the VLDB Endowment 15, no. 3 (November 2021): 504–12. http://dx.doi.org/10.14778/3494124.3494134.
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As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxonomy integration, a self-attentive spatial context extractor, and a distance-specific scoring function. Extensive experiments on two real-world datasets show that PRIM achieves the best results compared to state-of-the-art baselines and it is robust against data sparsity and is applicable to unseen cases in practice.
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Drury, Brett, Hugo Gonçalo Oliveira, and Alneu de Andrade Lopes. "A survey of the extraction and applications of causal relations." Natural Language Engineering 28, no. 3 (January 20, 2022): 361–400. http://dx.doi.org/10.1017/s135132492100036x.
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AbstractCausationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.
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Ma, Meng, Arielle Redfern, Xiang Zhou, Dan Li, Ying Ru, Kyeryoung Lee, Christopher Gilman, et al. "Automated abstraction of real-world clinical outcome in lung cancer: A natural language processing and artificial intelligence approach from electronic health records." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14062-e14062. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14062.
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e14062 Background: Real world evidence generated from electronic health records (EHRs) is playing an increasing role in health care decisions. It has been recognized as an essential element to assess cancer outcomes in real-world settings. Automatically abstracting outcomes from notes is becoming a fundamental challenge in medical informatics. In this study, we aim to develop a system to automatically abstract outcomes (Progression, Response, Stable Disease) from notes in lung cancer. Methods: A lung cancer cohort (n = 5,003) was obtained from the Mount Sinai Data Warehouse. The progress, pathology and radiology notes of patients were used. We integrated various techniques of Natural Language Processing (NLP) and Artificial Intelligence (AI) and developed a system to automatically abstract outcomes. The corresponding images, biopsies and lines of treatments (LOTs) were abstracted as attributes of outcomes. This system includes four information models: 1. Customized NLP annotator model: preprocessor, section detector, sentence splitter, named entity recognition, relation detector; CRF and LSTM methods were applied to recognize entities and relations. 2. Clinical Outcome container model: biopsy evidence extractor, lines of treatment detector, image evidence extractor, clinical outcome event recognizer, date detector, and temporal reasoning; Domain-specific rules were crafted to automatically infer outcomes. 3. Document Summarizer; 4. Longitudinal Outcome Summarizer. Results: To evaluate the outcomes abstracted, we curated a subset (n = 792) from patient cohort for which LOTs were available. About 61% of the outcomes identified were supported by radiologic images (time window = ±14 days) or biopsy pathology results (time window = ±100 days). In 91% (720/792) of patients, Progression was abstracted within a time window of 90 days prior to first-line treatment. Also, 72% of the Progression events identified were accompanied by a downstream event (e.g., treatment change or death). We randomly selected 250 outcomes for manual curation, and 197 outcomes were assessed to be correct (precision = 79%). Moreover, our automated abstraction system improved human abstractor efficiency to curate outcomes, reducing curation time per patient by 90%. Conclusions: We have demonstrated the feasibility and effectiveness of NLP and AI approaches to abstract outcomes from lung cancer EHR data. It promises to automatically abstract outcomes and other clinical entities from notes across all cancers.
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Halike, Ayiguli, Kahaerjiang Abiderexiti, and Tuergen Yibulayin. "Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method." Information 11, no. 1 (January 6, 2020): 31. http://dx.doi.org/10.3390/info11010031.
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Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.
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Zakria, Gehad, Mamdouh Farouk, Khaled Fathy, and Malak N. Makar. "Relation Extraction from Arabic Wikipedia." Indian Journal of Science and Technology 12, no. 46 (December 20, 2019): 01–06. http://dx.doi.org/10.17485/ijst/2019/v12i46/147512.
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Yadav, Shweta, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth. "“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware attention framework for relationship extraction." PLOS ONE 16, no. 3 (March 25, 2021): e0248299. http://dx.doi.org/10.1371/journal.pone.0248299.
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With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology) to jointly extract entities and their relationship using gated fusion sharing mechanism. Our model is further tailored to provide more focus to the entities mention in the sentence through entity-position aware attention layer, where ontology is used to locate the target entities position. Experimental results show that inclusion of the knowledge-aware attentive representation in association with BERT can extract the cannabis-depression relationship with better coverage in comparison to the state-of-the-art relation extractor.
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Husna, Shifa Aulia, Mochamad Hadi, and Rully Rahadian. "Struktur Komunitas Mikroartropoda Tanah di Lahan Pertanian Organik dan Anorganik di Desa Batur Kecamatan Getasan Salatiga." Bioma : Berkala Ilmiah Biologi 18, no. 2 (December 30, 2016): 157. http://dx.doi.org/10.14710/bioma.18.2.157-166.
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Soil microarthropods is a arthropods that have an important role in decomposing organic materials and soil nutrients. On the farmland there is organic materials content and soil nutrients that abundants enough, because the addition of manure as a source of energy in the ground. The research was conducted in August-October 2015 took place in the field of organic and inorganic farmland in the Batur Village, Getasan Sub-district, Salatiga. This study aimed to examine the soil microarthropods community structure in organic and inorganic farmland as well as the effect of chemical and physical environmental factors to community structure of soil microarthropods. The study was conducted with samples of soil sampling method (PCT) and extracted using Barlese Funnel Extractor. Statistically show that diversity of soil microarthropods between organic and inorganic farmland are not significantly different. There is found 28 taxa of soil microarthropods in organic farmland and 23 taxa in inorganic. The highest total individual density of the taxa are exist in the organic farmland (2260 individual/ m²). The highest abundance of soil microarthropods taxa in inorganic farmland are Carabidae (26,55%) dan Prostigmata (13,27%), while in inorganic farmland are Carabidae (17,24%) dan larva Coleoptera (13,79%). The evenness of soil microarthropods taxa in organic farmland are much low compared with inorganicfarmland, because there is a dominant taxa (Carabidae). There is an influence relation between the chemical and physical environmental factors including water content, porosity, nutrient, and organic materials with community structure of soil microarthropods. Key words:Community structure, Soil microarthropods, Organic and inorganic farmland
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Husna, Shifa Aulia, Mochamad Hadi, and Rully Rahadian. "Struktur Komunitas Mikroartropoda Tanah di Lahan Pertanian Organik dan Anorganik di Desa Batur Kecamatan Getasan Salatiga." Bioma : Berkala Ilmiah Biologi 18, no. 2 (February 16, 2017): 164. http://dx.doi.org/10.14710/bioma.18.2.164-173.
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Soil microarthropods is a arthropods that have an important role in decomposing organic materials and soil nutrients. On the farmland there is organic materials content and soil nutrients that abundants enough, because the addition of manure as a source of energy in the ground. The research was conducted in August-October 2015 took place in the field of organic and inorganic farmland in the Batur Village, Getasan Sub-district, Salatiga. This study aimed to examine the soil microarthropods community structure in organic and inorganic farmland as well as the effect of chemical and physical environmental factors to community structure of soil microarthropods. The study was conducted with samples of soil sampling method (PCT) and extracted using Barlese Funnel Extractor. Statistically show that diversity of soil microarthropods between organic and inorganic farmland are not significantly different. There is found 28 taxa of soil microarthropods in organic farmland and 23 taxa in inorganic. The highest total individual density of the taxa are exist in the organic farmland (2260 individual/ m²). The highest abundance of soil microarthropods taxa in inorganic farmland are Carabidae (26,55%) dan Prostigmata (13,27%), while in inorganic farmland are Carabidae (17,24%) dan larva Coleoptera (13,79%). The evenness of soil microarthropods taxa in organic farmland are much low compared with inorganicfarmland, because there is a dominant taxa (Carabidae). There is an influence relation between the chemical and physical environmental factors including water content, porosity, nutrient, and organic materials with community structure of soil microarthropods. Key words:Community structure, Soil microarthropods, Organic and inorganic farmland
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Min, Bonan, Shuming Shi, Ralph Grishman, and Chin-Yew Lin. "Towards Large-Scale Unsupervised Relation Extraction from the Web." International Journal on Semantic Web and Information Systems 8, no. 3 (July 2012): 1–23. http://dx.doi.org/10.4018/jswis.2012070101.
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The Web brings an open-ended set of semantic relations. Discovering the significant types is very challenging. Unsupervised algorithms have been developed to extract relations from a corpus without knowing the relation types in advance, but most rely on tagging arguments of predefined types. One recently reported system is able to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE (Information Extraction) algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar (“synonymous”) relation instances because of the sparseness of features. In this paper, the authors present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which they will show to be very effective for unsupervised relation extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the Web.
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HACHEY, B., C. GROVER, and R. TOBIN. "Datasets for generic relation extraction." Natural Language Engineering 18, no. 1 (March 9, 2011): 21–59. http://dx.doi.org/10.1017/s1351324911000106.
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AbstractA vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and notions of what constitutes a relation. We describe the preparation of corpora for comparative evaluation of relation extraction across domains based on the publicly available ACE 2004, ACE 2005 and BioInfer data sets. We present a common document type using token standoff and including detailed linguistic markup, while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web. For the ACE data, we describe an automatic process that automatically converts many relations involving nested, nominal entity mentions to relations involving non-nested, named or pronominal entity mentions. For example, the first entity is mapped from ‘one’ to ‘Amidu Berry’ in the membership relation described in ‘Amidu Berry, one half of PBS’. Moreover, we describe a comparably reannotated version of the BioInfer corpus that flattens nested relations, maps part-whole to part-part relations and maps n-ary to binary relations. Finally, we summarise experiments that compare approaches to generic relation extraction, a knowledge discovery task that uses minimally supervised techniques to achieve maximally portable extractors. These experiments illustrate the utility of the corpora.1
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Lorenzen, Marc C., Armin A. Weiser, Robert Pieper, Monika Lahrssen-Wiederholt, and Jorge Numata. "Introducing the Rapid Alert Supply Network Extractor (RASNEX) tool to mine supply chain information from food and feed contamination notifications in Europe." PLOS ONE 16, no. 7 (July 27, 2021): e0254301. http://dx.doi.org/10.1371/journal.pone.0254301.
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Background During food or feed contamination events, it is of utmost importance to ensure their rapid resolution to minimize impact on human health, animal health and finances. The existing Rapid Alert System for Food and Feed (RASFF) is used by the European Commission, national competent authorities of member countries and the European Food Safety Authority to report information on any direct or indirect human health risk arising from food or feed, or serious risks to animal health or the environment in relation to feed. Nevertheless, no methods exist to to collectively evaluate this vast source of supply chain information. Methods To aid in the extraction, evaluation and visualization of the data in RASFF notifications, we present the Rapid Alert Supply Network Extractor (RASNEX) open-source tool available from https://doi.org/10.5281/zenodo.4322555 freely. Among RASNEX’s functions is the graphical mapping of food and feed supply chain operators implicated in contamination events. RASNEX can be used during ongoing events as a support tool for risk analysis using RASFF notifications as input. Results In a first use case, we showcase the functionality of RASNEX with the RASFF notification on a 2017/2018 contamination event in eggs caused by the illegal use of fipronil. The information in this RASFF notification is used to visualize nine different flows of main and related food products. In a second use case, we combine RASFF notifications from different types of food safety hazards (Salmonella spp., fipronil and others) to obtain wider coverage of the visualized egg supply network compared to the first use case. Actors in the egg supply chain were identified mainly for Italy, Poland and Benelux. Other countries (although involved in the egg supply chain) were underrepresented. Conclusions We hypothesize that biases may be caused by inconsistent RASFF reporting behaviors by its members. These inconsistencies may be counteracted by implementing standardized decision-making tools to harmonize decisions whether to launch a RASFF notification, in turn resulting in a more uniform future coverage across European food and feed supply chains with RASNEX.
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Qu, R. L., D. Li, R. Du, and R. Qu. "Lead Uptake by Roots of Four Turfgrass Species in Hydroponic Cultures." HortScience 38, no. 4 (July 2003): 623–26. http://dx.doi.org/10.21273/hortsci.38.4.623.
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Turfgrass, which is widely grown and produces a large amount of biomass, could act as a sink for industrial pollutants in urban and suburban regions. Little research has been conducted regarding heavy metal uptake by turfgrasses. The objective of this study was to evaluate root uptake of lead (Pb) in four turfgrass species. Grasses were grown hydroponically in solutions containing from 0 to 450 mg·L-1 Pb, at either pH 4.5 or 5.5, for 4 or 8 days. A significant quadratic relation existed between Pb accumulation in roots and solution Pb concentration within the tested range. The maximum Pb accumulation in roots of the four species was in the range of 20 mg·g-1 dry root weight. Tall fescue (Festuca arundinacea Schreb.) and Spartina patens survived at 450 mg·L-1 Pb solution without showing obvious damage while centipedegrass [Eremochloa ophiuroides (Munro) Hack.] and buffalograss [Buchlöe dactyloides (Nutt.) Engelm.] deteriorated or died at this concentration. This study showed that turfgrass plants can absorb heavy metals efficiently and tolerate high Pb concentration in hydroponic solutions and thus may have a potential use in environmental remediation as a biological extractor of lead.
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Delgado-Plaza, Emérita, Miguel Quilambaqui, Juan Peralta-Jaramillo, Hector Apolo, and Borja Velázquez-Martí. "Estimation of the Energy Consumption of the Rice and Corn Drying Process in the Equatorial Zone." Applied Sciences 10, no. 21 (October 25, 2020): 7497. http://dx.doi.org/10.3390/app10217497.
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Drying is considered one of the industrial processes that requires more energy than other processes, being a topic of much interest to the agricultural sector, especially the evaluation of energy consumption for rice and corn dryers. To meet this goal, an overview survey matrix and protocols for temperature measurements of dryers were developed. The study evaluated 49 rice dryers and 14 yellow corn dryers. As a result, it was determined that the oversizing of the fan/extractor and the dryer engine generates a high energy consumption, added to the lack of insulation in the heat ducts. Therefore, the drying productivity index is very low in dryers using liquefied petroleum gas (LPG) being 0.14 dollar/quintal for rice and 0.27 dollar/quintal for corn and using biomass reaches 1.4 dollars/quintal. In relation to energy losses, these account for more than 55%. Inadequate energy management in drying processes directly influences the marketing chain of products, the losses of which are caused by fluctuations in the price of rice and corn on the domestic market, with the agricultural sector having to generate an energy efficiency plan.
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Zhou, Deyu, Dayou Zhong, and Yulan He. "Biomedical Relation Extraction: From Binary to Complex." Computational and Mathematical Methods in Medicine 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/298473.
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Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions.
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Galárraga-Espinosa, Daniela, Nabila Aghanim, Mathieu Langer, Céline Gouin, and Nicola Malavasi. "Populations of filaments from the distribution of galaxies in numerical simulations." Astronomy & Astrophysics 641 (September 2020): A173. http://dx.doi.org/10.1051/0004-6361/202037986.
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We present a statistical study of the filamentary structures of the cosmic web in the large hydro-dynamical simulations Illustris-TNG, Illustris, and Magneticum at redshift z = 0. We focus on the radial distribution of the galaxy density around filaments detected using the Discrete Persistent Structure Extractor (DisPerSE). We show that the average profile of filaments presents an excess of galaxy density (> 5σ) up to radial distances of 27 Mpc from the core. The relation between galaxy density and the length of filaments is further investigated showing that short (Lf < 9 Mpc) and long (Lf ≥ 20 Mpc) filaments are two statistically different populations. Short filaments are puffier, denser, and more connected to massive objects, whereas long filaments are thinner, less dense, and more connected to less massive structures. These two populations trace different environments and may correspond to bridges of matter between over-dense structures (short filaments), and to cosmic filaments shaping the skeleton of the cosmic web (long filaments). Through Markov chain Monte Carlo (MCMC) explorations, we find that the density profiles of both short and long filaments can be described by the same empirical models (generalised Navarro, Frenk and White, β-model, a single and a double power law) with different and distinct sets of parameters.
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YAN, YULAN, YUTAKA MATSUO, and MITSURU ISHIZUKA. "A NEW SHALLOW SEMANTIC PARSER FOR DESCRIBING THE CONCEPT STRUCTURE OF TEXT." International Journal of Semantic Computing 03, no. 01 (March 2009): 131–49. http://dx.doi.org/10.1142/s1793351x09000690.
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Recently, Semantic Role Labeling (SRL) systems have been used to examine a semantic predicate-argument structure for natural occurring texts. Facing the challenge of extracting a universal set of semantic or thematic relations covering various types of semantic relationships between entities, based on the Concept Description Language for Natural Language (CDL.nl) which defines a set of semantic relations to describe the concept structure of text, we develop a shallow semantic parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. The parsing task is a relation extraction process with two steps: relation detection and relation classification. Firstly, based on dependency analysis, a rule-based algorithm is presented to detect all entity pairs between each pair for which there exists a relationship; secondly, we use a kernel-based method to assign CDL.nl relations to detected entity pairs by leveraging diverse features. Preliminary evaluation on a manual dataset shows that CDL.nl relations can be extracted with good performance.
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Li, Fulin, Yuanbin Song, and Yongwei Shan. "Joint Extraction of Multiple Relations and Entities from Building Code Clauses." Applied Sciences 10, no. 20 (October 13, 2020): 7103. http://dx.doi.org/10.3390/app10207103.
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The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes.
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BOURGAIN, J. "MORE ON THE SUM-PRODUCT PHENOMENON IN PRIME FIELDS AND ITS APPLICATIONS." International Journal of Number Theory 01, no. 01 (March 2005): 1–32. http://dx.doi.org/10.1142/s1793042105000108.
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In this paper we establish new estimates on sum-product sets and certain exponential sums in finite fields of prime order. Our first result is an extension of the sum-product theorem from [8] when sets of different sizes are involed. It is shown that if [Formula: see text] and pε < |B|, |C| < |A| < p1-ε, then |A + B| + |A · C| > pδ (ε)|A|. Next we exploit the Szemerédi–Trotter theorem in finite fields (also obtained in [8]) to derive several new facts on expanders and extractors. It is shown for instance that the function f(x,y) = x(x+y) from [Formula: see text] to [Formula: see text] satisfies |F(A,B)| > pβ for some β = β (α) > α whenever [Formula: see text] and |A| ~ |B|~ pα, 0 < α < 1. The exponential sum ∑x∈ A,y∈Bεp(axy+bx2y2), ab ≠ 0 ( mod p), may be estimated nontrivially for arbitrary sets [Formula: see text] satisfying |A|, |B| > pρ where ρ < 1/2 is some constant. From this, one obtains an explicit 2-source extractor (with exponential uniform distribution) if both sources have entropy ratio at last ρ. No such examples when ρ < 1/2 seemed known. These questions were largely motivated by recent works on pseudo-randomness such as [2] and [3]. Finally it is shown that if pε < |A| < p1-ε, then always |A + A|+|A-1 + A-1| > pδ(ε)|A|. This is the finite fields version of a problem considered in [11]. If A is an interval, there is a relation to estimates on incomplete Kloosterman sums. In the Appendix, we obtain an apparently new bound on bilinear Kloosterman sums over relatively short intervals (without the restrictions of Karatsuba's result [14]) which is of relevance to problems involving the divisor function (see [1]) and the distribution ( mod p) of certain rational functions on the primes (cf. [12]).
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Peng, Nanyun, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. "Cross-Sentence N-ary Relation Extraction with Graph LSTMs." Transactions of the Association for Computational Linguistics 5 (December 2017): 101–15. http://dx.doi.org/10.1162/tacl_a_00049.
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Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.
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Lefever, Els, Marjan Van de Kauter, and Véronique Hoste. "HypoTerm." Terminology 20, no. 2 (October 31, 2014): 250–78. http://dx.doi.org/10.1075/term.20.2.06lef.
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HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracted domain- and user-specific terms from technical corpora, and generates a list of relations between these terms. This research study focused on the detection of hypernym relations between relevant terms and named entities. In order to detect all relevant hypernym relations in technical texts, we combined a lexico-syntactic pattern-based approach and a morpho-syntactic analyzer. To evaluate our relation finder, we constructed and manually annotated gold standard data for the dredging and financial domain in Dutch and English. The experimental results show that the HypoTerm system achieves high precision and recall figures for technical texts when starting from valid domain-specific terms and named entities. Thanks to this data-driven approach, it is possible to take an important step from terminology to concept extraction without using any external lexico-semantic resources.
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Huynh, Nghia Huu, Quoc Bao Ho, and Te An Nguyen. "An approach in health relation extraction." Science & Technology Development Journal - Economics - Law and Management 1, Q3 (December 31, 2017): 51–63. http://dx.doi.org/10.32508/stdjelm.v1iq3.449.
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Extracting relations among medical concepts is very important in the medical field. The relations denote the events or the possible relations between the concepts. Information about these relations provides users with a full view of medical problems. This helps physicians and health-care practitioners make effective decisions and minimize errors in the treatment process. This paper collects methods for relations extraction in health texts and presents an approach on one type of specific relation (i.e. template filling). The approach combines methods including rule-based and machine learningbased. The rule-based method uses the relation of semantic dependencies among the concepts to extract the rule set. The machine learning-based method uses the SVM (Support Vector Machine) algorithm and a feature set proposed. The results of the approach were estimated on an accuracy of 0.849.
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Shi, Yunyu, Jianfang Shan, Xiang Liu, and Yongxiang Xia. "Prior Knowledge-Based Event Network for Chinese Text." International Journal of Digital Multimedia Broadcasting 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/8594863.
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Text representation is a basic issue of text information processing and event plays an important role in text understanding; both attract the attention of scholars. The event network conceals lexical relations in events, and its edges express logical relations between events in document. However, the events and relations are extracted from event-annotated text, which makes it hard for large-scale text automatic processing. In the paper, with expanded CEC (Chinese Event Corpus) as data source, prior knowledge of manifestation rules of event and relation as the guide, we propose an event extraction method based on knowledge-based rule of event manifestation, to achieve automatic building and improve text processing performance of event network.
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Zhang, Xinsong, Pengshuai Li, Weijia Jia, and Hai Zhao. "Multi-Labeled Relation Extraction with Attentive Capsule Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7484–91. http://dx.doi.org/10.1609/aaai.v33i01.33017484.
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To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.
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Sokolov, Artem, Dmitry Valeev, and Aleksandr Kasikov. "Solvent Extraction of Iron(III) from Al Chloride Solution of Bauxite HCl Leaching by Mixture of Aliphatic Alcohol and Ketone." Metals 11, no. 2 (February 12, 2021): 321. http://dx.doi.org/10.3390/met11020321.
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Research into the solvent extraction of iron(III) from a chloride solution after bauxite HCl leaching by neutral oxygen-containing extractants and their mixtures were studied and the iron(III) extraction degree from chloride solutions using alcohols is presented. The effect of dilution of alcohol with a ketone by an extraction mixture in relation to its effectiveness was investigated. The iron(III) was efficiently extracted by the mixture of 1-octanol and 1-decanol (70%) with 2-undecanone (30%) from hydrochloric bauxite leach liquor at an O:A ratio = 2-4:1 at an iron(III) concentration of 7.4 g/L and 6 M HCl. For the removal of iron-containing organic phase from impurities (Al, Ca, Cr) that are co-extracted with iron(III), we used two step scrubbing at O:A = 5:1 by 7 M HCl as a scrub solution. The iron(III) stripping at the O:A ratio is shown. Using counter-current cascade of extractors, it was possible to obtain an FeCl3 solution with the iron(III) content of 90.5 g/L and total impurities less than 50 mg/L.
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AlArfaj, Abeer. "Towards relation extraction from Arabic text: a review." International Robotics & Automation Journal 5, no. 5 (December 24, 2019): 212–15. http://dx.doi.org/10.15406/iratj.2019.05.00195.
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Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.
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Bejan, Cosmin Adrian, Wei-Qi Wei, and Joshua C. Denny. "Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text." Journal of the American Medical Informatics Association 22, e1 (October 21, 2014): e162-e176. http://dx.doi.org/10.1136/amiajnl-2014-002954.
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Abstract Objective To evaluate the contribution of the MEDication Indication (MEDI) resource and SemRep for identifying treatment relations in clinical text. Materials and methods We first processed clinical documents with SemRep to extract the Unified Medical Language System (UMLS) concepts and the treatment relations between them. Then, we incorporated MEDI into a simple algorithm that identifies treatment relations between two concepts if they match a medication-indication pair in this resource. For a better coverage, we expanded MEDI using ontology relationships from RxNorm and UMLS Metathesaurus. We also developed two ensemble methods, which combined the predictions of SemRep and the MEDI algorithm. We evaluated our selected methods on two datasets, a Vanderbilt corpus of 6864 discharge summaries and the 2010 Informatics for Integrating Biology and the Bedside (i2b2)/Veteran's Affairs (VA) challenge dataset. Results The Vanderbilt dataset included 958 manually annotated treatment relations. A double annotation was performed on 25% of relations with high agreement (Cohen's κ = 0.86). The evaluation consisted of comparing the manual annotated relations with the relations identified by SemRep, the MEDI algorithm, and the two ensemble methods. On the first dataset, the best F1-measure results achieved by the MEDI algorithm and the union of the two resources (78.7 and 80, respectively) were significantly higher than the SemRep results (72.3). On the second dataset, the MEDI algorithm achieved better precision and significantly lower recall values than the best system in the i2b2 challenge. The two systems obtained comparable F1-measure values on the subset of i2b2 relations with both arguments in MEDI. Conclusions Both SemRep and MEDI can be used to extract treatment relations from clinical text. Knowledge-based extraction with MEDI outperformed use of SemRep alone, but superior performance was achieved by integrating both systems. The integration of knowledge-based resources such as MEDI into information extraction systems such as SemRep and the i2b2 relation extractors may improve treatment relation extraction from clinical text.
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Li, Dao Wang. "Research on Text Conceptual Relation Extraction Based on Domain Ontology." Advanced Materials Research 739 (August 2013): 574–79. http://dx.doi.org/10.4028/www.scientific.net/amr.739.574.
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At present, the ontology learning research focuses on the concept and relation extraction; the traditional extraction methods ignore the influence of the semantic factors on the extraction results, and lack of the accurate extraction of the relations among concepts. According to this problem, in this paper, the association rule is combined with the semantic similarity, and the improved comprehensive semantic similarity is applied into the relation extraction through the association rule mining relation. The experiments show that the relation extraction based on this method effectively improves the precision of the extraction results.
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Park, Seongsik, and Harksoo Kim. "Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence." Applied Sciences 10, no. 11 (June 1, 2020): 3851. http://dx.doi.org/10.3390/app10113851.
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Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we proposed a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject–object relations using a forward object decoder. Then, it finds 1-to-n subject–object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE (automatic content extraction) 2005 corpus and an F1-score of 78.3% for the NYT (New York Times) corpus.
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Sierra, Gerardo, Rodrigo Alarcón, César Aguilar, and Carme Bach. "Definitional verbal patterns for semantic relation extraction." Terminology 14, no. 1 (June 25, 2008): 74–98. http://dx.doi.org/10.1075/term.14.1.05sie.
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In this paper we present a description of the role of definitional verbal patterns for the extraction of semantic relations. Several studies show that semantic relations can be extracted from analytic definitions contained in machine-readable dictionaries (MRDs). In addition, definitions found in specialised texts are a good starting point to search for different types of definitions where other semantic relations occur. The extraction of definitional knowledge from specialised corpora represents another interesting approach for the extraction of semantic relations. Here, we present a descriptive analysis of definitional verbal patterns in Spanish and the first steps towards the development of a system for the automatic extraction of definitional knowledge.
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Lai, Qinghan, Zihan Zhou, and Song Liu. "Joint Entity-Relation Extraction via Improved Graph Attention Networks." Symmetry 12, no. 10 (October 21, 2020): 1746. http://dx.doi.org/10.3390/sym12101746.
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Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recognition task, the relation extraction task performs poorly on complex text. To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. In our proposed model, we introduced the graph attention network to extract entities and relations after graph embedding based on constructing symmetry relations. To mitigate the over-smoothing problem of graph convolutional networks, inspired by matrix factorization, we improved the graph attention network by designing a new multi-head attention mechanism and sharing attention parameters. To enhance the model robustness, we adopted the adversarial training to generate adversarial samples for training by adding tiny perturbations. Comparing with typical baseline models, we comprehensively evaluated our model by conducting experiments on an open domain dataset (CoNLL04) and a medical domain dataset (ADE). The experimental results demonstrate the effectiveness of ERIGAT in extracting entity and relation information.
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Ben Abdessalem Karaa, Wahiba, Eman H. Alkhammash, and Aida Bchir. "Drug Disease Relation Extraction from Biomedical Literature Using NLP and Machine Learning." Mobile Information Systems 2021 (May 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/9958410.
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Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical concepts, including protein and protein, gene and protein, drug and drug, and drug and disease. These relations can be extracted from biomedical literature available on various databases. This study examines the extraction of semantic relations that can occur between diseases and drugs. Findings will help specialists make good decisions when administering a medication to a patient and will allow them to continuously be up to date in their field. The objective of this work is to identify different features related to drugs and diseases from medical texts by applying Natural Language Processing (NLP) techniques and UMLS ontology. The Support Vector Machine classifier uses these features to extract valuable semantic relationships among text entities. The contributing factor of this research is the combination of the strength of a suggested NLP technique, which takes advantage of UMLS ontology and enables the extraction of correct and adequate features (frequency features, lexical features, morphological features, syntactic features, and semantic features), and Support Vector Machines with polynomial kernel function. These features are manipulated to pinpoint the relations between drug and disease. The proposed approach was evaluated using a standard corpus extracted from MEDLINE. The finding considerably improves the performance and outperforms similar works, especially the f-score for the most important relation “cure,” which is equal to 98.19%. The accuracy percentage is better than those in all the existing works for all the relations.
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Xiao, Ya, Chengxiang Tan, Zhijie Fan, Qian Xu, and Wenye Zhu. "Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9314–21. http://dx.doi.org/10.1609/aaai.v34i05.6471.
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Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences.
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Liu, Xiaofeng, Jianye Fan, and Shoubin Dong. "Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study." JMIR Medical Informatics 8, no. 5 (May 29, 2020): e17644. http://dx.doi.org/10.2196/17644.
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Background The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. Objective This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction. Methods This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics. Results Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance. Conclusions When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.
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Ye, Yuan, Yansong Feng, Bingfeng Luo, Yuxuan Lai, and Dongyan Zhao. "Integrating Relation Constraints with Neural Relation Extractors." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9442–49. http://dx.doi.org/10.1609/aaai.v34i05.6487.
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Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks (NNs). However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction (NRE) models even enhanced with extra post-processing. Our source code and datasets will be released at https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.
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Yu, Xinguo, Mingshu Wang, Wenbin Gan, Bin He, and Nan Ye. "A Framework for Solving Explicit Arithmetic Word Problems and Proving Plane Geometry Theorems." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 07 (June 7, 2019): 1940005. http://dx.doi.org/10.1142/s0218001419400056.
Full textAbstract:
This paper presents a framework for solving math problems stated in a natural language (NL) and applies the framework to develop algorithms for solving explicit arithmetic word problems and proving plane geometry theorems. We focus on problem understanding, that is, the transformation of a NL description of a math problem to a formal representation. We view this as a relation extraction problem, and adopt a greedy algorithm to extract the mathematical relations using a syntax-semantics model, which is a set of patterns describing how a syntactic pattern is mapped to its formal semantics. Our method yields a human readable solution that shows how the mathematical relations are extracted one at a time. We apply our framework to solve arithmetic word problems and prove plane geometry theorems. For arithmetic word problems, the extracted relations are transformed into a system of equations, and the equations are then solved to produce the solution. For plane geometry theorems, these extracted relations are input to an inference system to generate the proof. We evaluate our approach on a set of arithmetic word problems stated in Chinese, and two sets of plane geometry theorems stated in Chinese and English. Our algorithms achieve high accuracies on these datasets and they also show some desirable properties such as brevity of algorithm description and legibility of algorithm actions.
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Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630003. http://dx.doi.org/10.1142/s2425038416300032.
Full textAbstract:
Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation types such as Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic analysis. They expose simple yet principled ways in which verbs express relationships in linguistics such as verb phrase-based extraction or clause-based extraction. They obtain a significantly higher performance over previous systems in the first generation. In this paper, we describe an overview of two Open IE generations including strengths, weaknesses and application areas.