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Journal articles on the topic 'Paraphrase extraction'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

HO, CHUKFONG, MASRAH AZRIFAH AZMI MURAD, RABIAH ABDUL KADIR, and SHYAMALA DORAISAMY. "COMPARING TWO CORPUS-BASED METHODS FOR EXTRACTING PARAPHRASES TO DICTIONARY-BASED METHOD." International Journal of Semantic Computing 05, no. 02 (2011): 133–78. http://dx.doi.org/10.1142/s1793351x11001225.

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Paraphrase extraction plays an increasingly important role in language-related research and applications in areas such as information retrieval, question answering and automatic machine evaluation. Most of the existing methods extract paraphrases from different types of corpora by using syntactic-based approaches. Since a syntactic-based approach relies on the similarity of context to identify and capture paraphrases, other than paraphrases, other terms which tend to appear in a similar context such as loosely related terms and functionally similar yet unrelated terms tend to be extracted. Bes

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Pöckelmann, Marcus, Janis Dähne, Jörg Ritter, and Paul Molitor. "Fast paraphrase extraction in Ancient Greek literature." it - Information Technology 62, no. 2 (2020): 75–89. http://dx.doi.org/10.1515/itit-2019-0042.

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AbstractIn this paper,A shorter version of the paper appeared in German in the final report of the Digital Plato project which was funded by the Volkswagen Foundation from 2016 to 2019. [35], [28]. we present a method for paraphrase extraction in Ancient Greek that can be applied to huge text corpora in interactive humanities applications. Since lexical databases and POS tagging are either unavailable or do not achieve sufficient accuracy for ancient languages, our approach is based on pure word embeddings and the word mover’s distance (WMD) [20]. We show how to adapt the WMD approach to parap

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Chitra, A., and Anupriya Rajkumar. "Paraphrase Extraction using fuzzy hierarchical clustering." Applied Soft Computing 34 (September 2015): 426–37. http://dx.doi.org/10.1016/j.asoc.2015.05.017.

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VILA, M., H. RODRÍGUEZ, and M. A. MARTÍ. "Relational paraphrase acquisition from Wikipedia: The WRPA method and corpus." Natural Language Engineering 21, no. 3 (2013): 355–89. http://dx.doi.org/10.1017/s1351324913000235.

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AbstractParaphrase corpora are an essential but scarce resource in Natural Language Processing. In this paper, we present the Wikipedia-based Relational Paraphrase Acquisition (WRPA) method, which extracts relational paraphrases from Wikipedia, and the derived WRPA paraphrase corpus. The WRPA corpus currently covers person-related and authorship relations in English and Spanish, respectively, suggesting that, given adequate Wikipedia coverage, our method is independent of the language and the relation addressed. WRPA extracts entity pairs from structured information in Wikipedia applying dista

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Recasens, Marta, and Marta Vila. "On Paraphrase and Coreference." Computational Linguistics 36, no. 4 (2010): 639–47. http://dx.doi.org/10.1162/coli_a_00014.

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By providing a better understanding of paraphrase and coreference in terms of similarities and differences in their linguistic nature, this article delimits what the focus of paraphrase extraction and coreference resolution tasks should be, and to what extent they can help each other. We argue for the relevance of this discussion to Natural Language Processing.

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ZHAO, Shi-Qi, Lin ZHAO, Ting LIU, and Sheng LI. "Paraphrase Collocation Extraction Based on Binary Classification." Journal of Software 21, no. 6 (2010): 1267–76. http://dx.doi.org/10.3724/sp.j.1001.2010.03586.

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Mauro Mirto, Ignazio. "Automatic Extraction of Semantic Roles in Support Verb Constructions." International Journal on Natural Language Computing 10, no. 03 (2021): 1–10. http://dx.doi.org/10.5121/ijnlc.2021.10301.

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This paper deals with paraphrastic relations in Italian. In the following sentences: (a) Max strappò delle lacrime a Sara 'Max moved Sara to tears' and (b) Max fece piangere Sara 'Max made Sara cry', the verbs differ syntactically and semantically. Strappare 'tear/rip/wring' is transitive, fare ‘have/make’ is a causative, and piangere 'cry' is intransitive. Despite this, a translation of (a) as (b) is legitimate and therefore (a) is a paraphrase of (b). In theoretical linguistics this raises an issue concerning the relationship between strappare and fare/piangere in Italian, and that in Englis

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Hu Hongsi, Zhang Wenbo, and Yao Tianfang. "Paraphrase Extraction from Interactive Q&A Communities." International Journal of Information Processing and Management 4, no. 2 (2013): 45–52. http://dx.doi.org/10.4156/ijipm.vol4.issue2.6.

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Glazkova, Anna Valer'evna. "Statistical evaluation of the information content of attributes for the task of searching for semantically close sentences." Программные системы и вычислительные методы, no. 1 (January 2020): 8–17. http://dx.doi.org/10.7256/2454-0714.2020.1.31728.

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The paper presents the results of evaluating the informative value of quantitative and binary signs to solve the problem of finding semantically close sentences (paraphrases). Three types of signs are considered in the article: those built on vector representations of words (according to the Word2Vec model), based on the extraction of numbers and structured information and reflecting the quantitative characteristics of the text. As indicators of information content, the percentage of paraphrases among examples with a characteristic, and the percentage of paraphrases with a attribute (for binar

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박에스더, 임해창, 김민정, and 이형규. "Pivot Discrimination Approach for Paraphrase Extraction from Bilingual Corpus." Korean Journal of Cognitive Science 22, no. 1 (2011): 57–78. http://dx.doi.org/10.19066/cogsci.2011.22.1.004.

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Sierra Martínez, Gerardo Eugenio, and Gemma Bel Enguix. "The Bible as a Corpus for Language Technologies." Signos Lingüísticos 20, no. 39 (2024): 136–67. https://doi.org/10.24275/sling.v20n39.05.

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"This work aims to create an aligned corpus of eleven Spanish translations of the Bible to advance computational linguistics in Spanish. The use of this corpus is essential for applications such as paraphrase detection, lexical grouping identification, and language model evaluation for search systems. In this way, the study covers various aspects of natural language processing, including similarity, lexical extraction, and bias analysis, with the goal of promoting the development of language technologies in Spanish.Keywords: Linguistic corpus; natural language processing; paraphrase detection;

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Mahmoud, Adnen, and Mounir Zrigui. "Distributional Semantic Model Based on Convolutional Neural Network for Arabic Textual Similarity." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (2020): 35–50. http://dx.doi.org/10.4018/ijcini.2020010103.

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The problem addressed is to develop a model that can reliably identify whether a previously unseen document pair is paraphrased or not. Its detection in Arabic documents is a challenge because of its variability in features and the lack of publicly available corpora. Faced with these problems, the authors propose a semantic approach. At the feature extraction level, the authors use global vectors representation combining global co-occurrence counting and a contextual skip gram model. At the paraphrase identification level, the authors apply a convolutional neural network model to learn more co

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Aguilar, Jose, Camilo Salazar, Henry Velasco, Julian Monsalve-Pulido, and Edwin Montoya. "Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents." Computation 8, no. 2 (2020): 30. http://dx.doi.org/10.3390/computation8020030.

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This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The util

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Choi, Sung-Pil, and Sung-Hyon Myaeng. "Terminological paraphrase extraction from scientific literature based on predicate argument tuples." Journal of Information Science 38, no. 6 (2012): 593–611. http://dx.doi.org/10.1177/0165551512459920.

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DORR, BONNIE J., REBECCA J. PASSONNEAU, DAVID FARWELL, et al. "Interlingual annotation of parallel text corpora: a new framework for annotation and evaluation." Natural Language Engineering 16, no. 3 (2010): 197–243. http://dx.doi.org/10.1017/s1351324910000070.

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AbstractThis paper focuses on an important step in the creation of a system of meaning representation and the development of semantically annotated parallel corpora, for use in applications such as machine translation, question answering, text summarization, and information retrieval. The work described below constitutes the first effort of any kind to annotate multiple translations of foreign-language texts with interlingual content. Three levels of representation are introduced: deep syntactic dependencies (IL0), intermediate semantic representations (IL1), and a normalized representation th

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Xu, Wei, Alan Ritter, Chris Callison-Burch, William B. Dolan, and Yangfeng Ji. "Extracting Lexically Divergent Paraphrases from Twitter." Transactions of the Association for Computational Linguistics 2 (December 2014): 435–48. http://dx.doi.org/10.1162/tacl_a_00194.

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We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter. We jointly model paraphrase relations between word and sentence pairs and assume only sentence-level annotations during learning. Using this principled latent variable model alone, we achieve the performance competitive with a state-of-the-art method which combines a latent space model with a feature-based supervised classifier. Our model also captures lexically divergent paraphrases that differ from yet complement previous methods; combining our model

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DIAS, GAËL, RUMEN MORALIYSKI, JOÃO CORDEIRO, ANTOINE DOUCET, and HELENA AHONEN-MYKA. "Automatic discovery of word semantic relations using paraphrase alignment and distributional lexical semantics analysis." Natural Language Engineering 16, no. 4 (2010): 439–67. http://dx.doi.org/10.1017/s135132491000015x.

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AbstractThesauri, which list the most salient semantic relations between words, have mostly been compiled manually. Therefore, the inclusion of an entry depends on the subjective decision of the lexicographer. As a consequence, those resources are usually incomplete. In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal differs from all other research presented so far as it tries to take the best of two

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Dai, Yu, Yuqiao Liu, Lei Yang, and Yufan Fu. "An Idiom Reading Comprehension Model Based on Multi-Granularity Reasoning and Paraphrase Expansion." Applied Sciences 13, no. 9 (2023): 5777. http://dx.doi.org/10.3390/app13095777.

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Idioms are a unique class of words in the Chinese language that can be challenging for Chinese machine reading comprehension due to their formal simplicity and the potential mismatch between their literal and figurative meanings. To address this issue, this paper adopted the “2 + 2” structure as the representation model for idiom structure feature extraction. According to the linguistic theory of idioms, to enhance the model’s learning ability for idiom semantics, we propose a two-stage semantic expansion method that leverages semantic knowledge during the pre-training stage and extracts idiom

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Diedrichsen, Elke. "Linguistic challenges in automatic summarization technology." Journal of Computer-Assisted Linguistic Research 1, no. 1 (2017): 40. http://dx.doi.org/10.4995/jclr.2017.7787.

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Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse

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Vetriselvi, T., and Mihir Mathur. "Text Summarization and Translation of Summarized Outcome in French." E3S Web of Conferences 399 (2023): 04002. http://dx.doi.org/10.1051/e3sconf/202339904002.

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Automatic text summarization is increasingly required with the exponential growth of unstructured text through increasing internet and social media usage across the globe. The various approaches are outcomes of extraction-based and abstraction-based. In Extraction-based summarization, the extracted content from the original data, is typically presented in the same or slightly modified form without significant paraphrasing or restructuring. Abstractive methods involve building an internal representation of the original content and then using that representation to generate a summary that may no

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Taghizadeh, Nasrin, and Heshaam Faili. "Cross-lingual Adaptation Using Universal Dependencies." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (2021): 1–23. http://dx.doi.org/10.1145/3448251.

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We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. The idea of UD parsing is to capture similarities as well as idiosyncrasies among typologically different languages. In this article, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages. We study two tasks of paraphrase identification and relation extraction as case studies. Based on UD parse trees, we develop several models

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Madnani, Nitin, and Bonnie J. Dorr. "Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods." Computational Linguistics 36, no. 3 (2010): 341–87. http://dx.doi.org/10.1162/coli_a_00002.

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The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language—words, phrases, and sentences—is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications. In this article, we attempt to conduct a comprehensive and application-independent survey of data-driven phrasal and sentential paraphrase generation methods, while also conveying an appreciation for the importance and potenti

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Kanchan Babaji Dhomse. "Dynamic Question Generation using NER with various Feature Extraction and NLP Techniques." Advances in Nonlinear Variational Inequalities 27, no. 3 (2024): 639–52. http://dx.doi.org/10.52783/anvi.v27.1432.

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The purpose of the Automatic Question Generator is to produce novel questions from the given text that are both linguistically natural, semantically precise, and syntactically coherent. Unlike activities such as summarization and paraphrase, replies play a vital role in question-answering tasks. The creation of multiple-choice questions involves the use of distractions of high quality and the formulation of successful questions. This technology enables instructors to generate multiple-choice assessment questions including both right answers and distractors. An instructor may promptly evaluate

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Gates, Kelly. "Policing as Digital Platform." Surveillance & Society 17, no. 1/2 (2019): 63–68. http://dx.doi.org/10.24908/ss.v17i1/2.12940.

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Much of the discussion about platforms and “platform capitalism” centers on commercial platform companies like Google, Facebook, Amazon, and Apple. Shoshana Zuboff’s (2015) analysis of “surveillance capitalism” similarly focuses on Google as the trailblazer pushing the new logic of accumulation that is focused on data extraction and analysis of human activities. In his typology of platform companies, Nick Srnicek (2017) includes less visible industrial platforms that situate themselves as intermediaries between companies rather than between companies and consumer-users. In this article, the fo

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Chitra, A., and Anupriya Rajkumar. "Plagiarism Detection Using Machine Learning-Based Paraphrase Recognizer." Journal of Intelligent Systems 25, no. 3 (2016): 351–59. http://dx.doi.org/10.1515/jisys-2014-0146.

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AbstractPlagiarism in free text has become a common occurrence due to the wide availability of voluminous information resources. Automatic plagiarism detection systems aim to identify plagiarized content present in large repositories. This task is rendered difficult by the use of sophisticated plagiarism techniques such as paraphrasing and summarization, which mask the occurrence of plagiarism. In this work, a monolingual plagiarism detection technique has been developed to tackle cases of paraphrased plagiarism. A support vector machine based paraphrase recognition system, which works by extr

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ZHAO, SHIQI, HAIFENG WANG, TING LIU, and SHENG LI. "Extracting paraphrase patterns from bilingual parallel corpora." Natural Language Engineering 15, no. 4 (2009): 503–26. http://dx.doi.org/10.1017/s1351324909990155.

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AbstractParaphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented metho

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Anchiêta, Rafael T., Rogério F. de Sousa, and Thiago A. S. Pardo. "Modeling the Paraphrase Detection Task over a Heterogeneous Graph Network with Data Augmentation." Information 11, no. 9 (2020): 422. http://dx.doi.org/10.3390/info11090422.

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Paraphrase detection is a Natural-Language Processing (NLP) task that aims at automatically identifying whether two sentences convey the same meaning (even with different words). For the Portuguese language, most of the works model this task as a machine-learning solution, extracting features and training a classifier. In this paper, following a different line, we explore a graph structure representation and model the paraphrase identification task over a heterogeneous network. We also adopt a back-translation strategy for data augmentation to balance the dataset we use. Our approach, although

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Figueroa, Alejandro, and Guenter Neumann. "Learning to Rank Effective Paraphrases from Query Logs for Community Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1099–105. http://dx.doi.org/10.1609/aaai.v27i1.8453.

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We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Yahoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of queries and questions using the query-question click history. Elements of this corpus are automatically ranked according to recall and mean reciprocal rank, and then used for learning two independent learning to rank models (SVMRank), whereby a set of new query paraphrases can be scored according to recall and MRR. We perform several automatic evaluation proced

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Ho, ChukFong, Masrah Azrifah Azmi Murad, Shyamala Doraisamy, and Rabiah Abdul Kadir. "Extracting lexical and phrasal paraphrases: a review of the literature." Artificial Intelligence Review 42, no. 4 (2012): 851–94. http://dx.doi.org/10.1007/s10462-012-9357-8.

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Keshtkar, Fazel, and Diana Inkpen. "A BOOTSTRAPPING METHOD FOR EXTRACTING PARAPHRASES OF EMOTION EXPRESSIONS FROM TEXTS." Computational Intelligence 29, no. 3 (2012): 417–35. http://dx.doi.org/10.1111/j.1467-8640.2012.00458.x.

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Kirmani, Mahira, Gagandeep Kaur, and Mudasir Mohd. "Analysis of Abstractive and Extractive Summarization Methods." International Journal of Emerging Technologies in Learning (iJET) 19, no. 01 (2024): 86–96. http://dx.doi.org/10.3991/ijet.v19i01.46079.

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This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, th

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K, Manjula, and M. B.Anandaraju. "A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)." International Journal of Engineering & Technology 7, no. 1.9 (2018): 132. http://dx.doi.org/10.14419/ijet.v7i1.9.9749.

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Brain Computer Interfacing (BCI) is a methodology which imparts a path for communication from external world using brain signals through computer. BCI identifies the specific patterns in a person’s changing brain activity to initiate control which relates to the person’s intention. The BCI system paraphrases these signal patterns into meaningful control command. In evolving BCI system, numerous signal processing algorithms are proposed. Non-invasive Electroencephalogram (EEG) signals or mind waves are used to extract the distinguished features and further they are classified choosing an approp

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Jaisankar, Vijay, Sambaran Bandyopadhyay, Kalp Vyas, Varre Suman Chaitanya, and Shwetha Somasundaram. "Deep Submodular Optimization and LLM for Multimodal Content Extraction and Automatic Poster Generation from Long Document." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24221–29. https://doi.org/10.1609/aaai.v39i23.34598.

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A poster from a long input document can be considered as a one-page easy-to-read multimodal (text and images) summary presented on a nice template with good design elements. Automatic transformation of a long document into a poster is a very less studied but challenging task. It involves content summarization of the input document followed by template generation and harmonization. In this work, we propose a novel deep submodular function which can be trained on ground truth summaries to extract multimodal content from the document and explicitly ensures good coverage, diversity and alignment o

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Zhang, Yujie, Jiaqi Fu, Jie Lai, et al. "Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review." Journal of Medical Internet Research 26 (May 17, 2024): e51496. http://dx.doi.org/10.2196/51496.

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Background The internet community has become a significant source for researchers to conduct qualitative studies analyzing users’ views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. Objective This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. Methods We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These s

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SZPEKTOR, IDAN, HRISTO TANEV, IDO DAGAN, BONAVENTURA COPPOLA, and MILEN KOUYLEKOV. "Unsupervised acquisition of entailment relations from the Web." Natural Language Engineering 21, no. 1 (2013): 3–47. http://dx.doi.org/10.1017/s1351324913000156.

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AbstractEntailment recognition is a primary generic task in natural language inference, whose focus is to detect whether the meaning of one expression can be inferred from the meaning of the other. Accordingly, many NLP applications would benefit from high coverage knowledgebases of paraphrases and entailment rules. To this end, learning such knowledgebases from the Web is especially appealing due to its huge size as well as its highly heterogeneous content, allowing for a more scalable rule extraction of various domains. However, the scalability of state-of-the-art entailment rule acquisition

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Kurihara, Kosuke, Yoshiyuki Shoji, Sumio Fujita, and Martin J. Durst. "Doc2Vec-based Approach for Extracting Diverse Evaluation Expressions from Online Review Data." Journal of Data Intelligence 3, no. 4 (2022): 441–59. http://dx.doi.org/10.26421/jdi3.4-3.

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This paper proposes a method for extracting diverse expressions from online movie review texts for a given keyword query. When people watch a movie that makes them cry, they generally do not say ``I cried.'' Instead, they use such euphemistic language as ``I needed a handkerchief'' or ``My makeup was running.''To enable information retrieval based on audience reactions such as ``movies that make me cry'' using review texts, various paraphrased expressions must be collected for arbitrary queries. Our proposed method extracts such expressions from review datasets by applying two extensions to Do

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DAGAN, IDO, BILL DOLAN, BERNARDO MAGNINI, and DAN ROTH. "Recognizing textual entailment: Rational, evaluation and approaches." Natural Language Engineering 15, no. 4 (2009): i—xvii. http://dx.doi.org/10.1017/s1351324909990209.

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AbstractThe goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in applications such as question answering, inform

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Hennigan, Máiréad, Simon Henderson, and Ezra Burke. "When Occam's razor loses its edge: the simplest explanation isn't always correct." Dental Update 50, no. 6 (2023): 527–30. http://dx.doi.org/10.12968/denu.2023.50.6.527.

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Dental infections are common in children. Occam's razor, typically paraphrased, suggests that the simplest solution is most likely the right one. We report a case of an 11-year-old child who presented with right-sided facial swelling, fever, trismus, and a heavily broken-down right maxillary molar with a large apical radiolucency. After admitting the child, intravenous antibiotics and fluids were prescribed in preparation for the extraction of the UR6 and LR6 in theatre early the next morning. However, 9 hours later, before surgery, the patient unexpectedly and rapidly deteriorated neurologica

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Ahmed, Mahtab, and Robert E. Mercer. "Modelling Sentence Pairs via Reinforcement Learning: An Actor-Critic Approach to Learn the Irrelevant Words." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7358–66. http://dx.doi.org/10.1609/aaai.v34i05.6230.

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Learning sentence representation is a fundamental task in Natural Language Processing. Most of the existing sentence pair modelling architectures focus only on extracting and using the rich sentence pair features. The drawback of utilizing all of these features makes the learning process much harder. In this study, we propose a reinforcement learning (RL) method to learn a sentence pair representation when performing tasks like semantic similarity, paraphrase identification, and question-answer pair modelling. We formulate this learning problem as a sequential decision making task where the de

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Zhang, Haiyu, Yinghui Zhao, Boyu Sun, Yaqi Wu, Zetian Fu, and Xinqing Xiao. "Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles." Applied Sciences 15, no. 7 (2025): 4034. https://doi.org/10.3390/app15074034.

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In recent years, the rapid development of the new energy vehicle (NEV) industry has exposed significant deficiencies in intelligent fault diagnosis and information retrieval technologies, especially in intelligent fault information retrieval, which faces persistent challenges including inadequate system adaptability and reasoning bottlenecks. To address these challenges, this study proposes a Retrieval-Augmented Generation (RAG) framework that integrates large language models (LLMs) with knowledge graphs (KGs). The framework consists of three key components: fault data collection, knowledge gr

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Baştürk, Burcu, and Aytuğ Onan. "Discovering Latent Themes in Heart Disease Article Abstracts: A Topic Modeling Approach." Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 27, no. 80 (2025): 216–23. https://doi.org/10.21205/deufmd.2025278007.

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Heart disease is a global public health problem that requires in-depth analysis of extensive literature to uncover specific themes and relationships. This study aimed to identify latent themes and calculate consistencies in 5,000 heart disease-related abstracts retrieved from PubMed using topic modeling techniques. The original abstracts were paraphrased using ChatGPT and NLTK(Natural Language Toolkit), followed by extensive preprocessing, including tokenization, removal of stopped words, stemming, and lemmatization. For effective feature extraction, text data was vectorized using TF-IDF (term

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Guan, Xiaohan, Jianhui Han, Zhi Liu, and Mengmeng Zhang. "Sentence Similarity Algorithm Based on Fused Bi-Channel Dependency Matching Feature." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 07 (2019): 2050019. http://dx.doi.org/10.1142/s0218001420500196.

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Many tasks of natural language processing such as information retrieval, intelligent question answering, and machine translation require the calculation of sentence similarity. The traditional calculation methods used in the past could not solve semantic understanding problems well. First, the model structure based on Siamese lack of interaction between sentences; second, it has matching problem which contains lacking position information and only using partial matching factor based on the matching model. In this paper, a combination of word and word’s dependence is proposed to calculate the s

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Shuvalov, Petr. "Die Blonden des 11. Buches des Pseudo-Maurikios." Amsterdamer Beiträge zur älteren Germanistik 80, no. 1-2 (2020): 108–33. http://dx.doi.org/10.1163/18756719-12340182.

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Abstract This analysis of the text of Pseudo-Maurice’s Strategikon ch. xi,3, discussing the “light-haired peoples,” is based on a new investigation of the MSS by the on-line photocopies, and shows that in the text there are many inner citations and paraphrases as well as some traces of redactions previous to the archetype (i.e. common ancestor of the MSS). The analysis of the punctuation allows to propose the hypothesis that the cola in Leo’s Problemata do reflect directly the system of punctuation in the hyparchetype α (i.e. the ancestor of β, which is the progenitor of the main MSS). The tex

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Chettukindi, Sathwik. "Question Generator and Text Summarizer Using NLP." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1767–73. http://dx.doi.org/10.22214/ijraset.2023.50477.

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Abstract: The main objective of this project is to develop an application to generate the questions from a given passage or paragraphs. This project is an application of Natural Language Processing (NLP). The application generates different types of questions such as MCQ’s. The current project helps the teachers to prepare questions to the examinations, quizzes etc. It also helps the students to get summary from the text they provide. The summary would be generated from given text then, the application identifies key concept in summary. It identifies key words from sentences and generates MCQ’

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Chougule, Mr Sumit, Mr Priyansh Dudhabale, and Mr Tejas Havaldar. "Deep Learning based Text Abstraction." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4559–66. http://dx.doi.org/10.22214/ijraset.2023.52463.

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Abstract: Text abstraction based on deep learning has proven to be a promising method for the task of extracting large amounts of text while preserving the most important information. This article provides an overview of text abstraction based on deep learning, highlighting various techniques and applications in this field. This article reviews the existing literature on text abstraction based on deep learning, focusing on various methods such as sentence compression, text summarization, and paraphrase, and compares their advantages and disadvantages. The article also describes various deep le

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Kaur, Rajpal. "Enhancing Technical Documentation through Intelligent Text Summarization Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1624–35. https://doi.org/10.22214/ijraset.2025.73052.

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In an era of fast digital transformation, technical documentation is more important than ever in aiding user knowledge, upkeep of systems, and operational efficiency across a variety of organizations. However, the ever-growing complexity of software platforms, enterprise applications, and IT infrastructures has resulted in a massive amount of technical content that is challenging to navigate and time-consuming to comprehend. Users, including developers, executives, end users, and support engineers, deserve accurate and easily accessible documentation. This study investigates the use of text su

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Bittnerová, Dana. "Téma dopisu v naivní poezii dospívajících dívek." Lidé města 1, no. 1/1 (1999): 128–36. http://dx.doi.org/10.14712/12128112.4005.

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The life of children within the framework of the culture of adult people in the 20th century has been influenced by the adoption of a number of cultural phenomena and it has brought about the widening of formal as well as content parts of the folklore. As a result, the children's literary tradltion includes not only literary forms which are conveyed verbally. Small literary works of scriptural culture are of the same validity and importance. All phenomena currently regarded as part of children's scriptural culture have their model in the culture of adult people. Those with the most stable cont

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Gupta, Dhruv. "Search and analytics using semantic annotations." ACM SIGIR Forum 53, no. 2 (2019): 100–101. http://dx.doi.org/10.1145/3458553.3458567.

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Current information retrieval systems are limited to text in documents for helping users with their information needs. With the progress in the field of natural language processing, there now exists the possibility of enriching large document collections with accurate semantic annotations. Annotations in the form of part-of-speech tags, temporal expressions, numerical values, geographic locations, and other named entities can help us look at terms in text with additional semantics. This doctoral dissertation presents methods for search and analysis of large semantically annotated document coll

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Мелик-Гайказян, Ирина Вигеновна. "BIOETHICS AND SEMIOTICS: INSTEAD OF A FOREWORD." ΠΡΑΞΗMΑ. Journal of Visual Semiotics, no. 3(29) (June 18, 2021): 9–18. http://dx.doi.org/10.23951/2312-7899-2021-3-9-18.

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Обстоятельства помешали научному редактору номера – Елене Георгиевне Гребенщиковой – написать предисловие. В нашем молодом журнале есть уже своя традиция: научный редактор предваряет номер концептуальной преамбулой к статьям, посвященных обсуждению различных аспектов одной проблемы. Авторов данного номера объединяют исследовательские и организационные обстоятельства. Все мы были вовлечены в исследовательское поле биоэтики Борисом Григорьевичем Юдиным. Привлечение же методологических потенциалов семиотики для решения задач биоэтики произошло на «томской почве» как результат организации серий ко

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Kanerva, Jenna, Filip Ginter, Li-Hsin Chang, et al. "Towards diverse and contextually anchored paraphrase modeling: A dataset and baselines for Finnish." Natural Language Engineering, March 16, 2023, 1–35. http://dx.doi.org/10.1017/s1351324923000086.

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Abstract In this paper, we study natural language paraphrasing from both corpus creation and modeling points of view. We focus in particular on the methodology that allows the extraction of challenging examples of paraphrase pairs in their natural textual context, leading to a dataset potentially more suitable for evaluating the models’ ability to represent meaning, especially in document context, when compared with those gathered using various sentence-level heuristics. To this end, we introduce the Turku Paraphrase Corpus, the first large-scale, fully manually annotated corpus of paraphrases

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