Academic literature on the topic 'Natural Language Information Analysis Method (NIAM)'
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Journal articles on the topic "Natural Language Information Analysis Method (NIAM)"
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Din, Roshidi, Rosmadi Bakar, Raihan Sabirah Sabri, Mohamad Yusof Darus, and Shamsul Jamel Elias. "Performance analysis on secured data method in natural language steganography." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 298–304. http://dx.doi.org/10.11591/eei.v8i1.1441.
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The rapid amount of exchange information that causes the expansion of the internet during the last decade has motivated that a research in this field. Recently, steganography approaches have received an unexpected attention. Hence, the aim of this paper is to review different performance metric; covering the decoding, decrypting and extracting performance metric. The process of data decoding interprets the received hidden message into a code word. As such, data encryption is the best way to provide a secure communication. Decrypting take an encrypted text and converting it back into an original text. Data extracting is a process which is the reverse of the data embedding process. The effectiveness evaluation is mainly determined by the performance metric aspect. The intention of researchers is to improve performance metric characteristics. The evaluation success is mainly determined by the performance analysis aspect. The objective of this paper is to present a review on the study of steganography in natural language based on the criteria of the performance analysis. The findings review will clarify the preferred performance metric aspects used. This review is hoped to help future research in evaluating the performance analysis of natural language in general and the proposed secured data revealed on natural language steganography in specific.
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Chen, Jinyan, Susanne Becken, and Bela Stantic. "Lexicon based Chinese language sentiment analysis method." Computer Science and Information Systems 16, no. 2 (2019): 639–55. http://dx.doi.org/10.2298/csis181015013c.
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The growing number of social media users and vast volume of posts could provide valuable information about the sentiment toward different locations, services as well as people. Recent advances in Big Data analytics and natural language processing often means to automatically calculate sentiment in these posts. Sentiment analysis is challenging and computationally demanding task due to the volume of data, misspelling, emoticons as well as abbreviations. While significant work was directed toward the sentiment analysis of English text there is limited attention in literature toward the sentiment analytic of Chinese language. In this work we propose method to identify the sentiment in Chinese social media posts and to test our method we rely on posts sent by visitors of Great Barrier Reef by users of most popular Chinese social media platform Sina Weibo. We elaborate process of capturing of weibo posts, describe a creation of lexicon as well as develop and explain algorithm for sentiment calculation. In case study, related to sentiment toward the different GBR destinations, we demonstrate that the proposed method is effective in obtaining the information and is suitable to monitor visitors? opinion.
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CHIU, IVEY, and L. H. SHU. "Biomimetic design through natural language analysis to facilitate cross-domain information retrieval." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 45–59. http://dx.doi.org/10.1017/s0890060407070138.
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Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers' knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean/remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean/remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean/remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.
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Akita, Chie, Motohiro Mase, and Yasuhiko Kitamura. "Natural Language Questions and Answers for RDF Information Resources." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 4 (May 20, 2010): 384–89. http://dx.doi.org/10.20965/jaciii.2010.p0384.
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We propose Questions and Answers (Q&A) method responding natural language questions about RDF information resource. When a natural language question and an RDF graph are given, keywords are extracted from the question using morphological analysis and keywords are converted to key elements referencing lexica describing correspondence relationships between keywords and elements. A question subgraph containing all key elements corresponding to keywords in the question are extracted from the RDF graph and the question subgraph is searched for an answer. We evaluate performance using an RDF information resource that describes our laboratory.
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Litvin, A. A., V. Yu Velychko, and V. V. Kaverynskyi. "Method of information obtaining from ontology on the basis of a natural language phrase analysis." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2020): 322–30. http://dx.doi.org/10.15407/pp2020.02-03.322.
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A method for phrases analyzing in natural languages of inflective type (Ukrainian and Russian) has been developed. The method allows one to outline main expressed ideas and groups of words in the text by which they are stated. The semantic trees of propositions formed in this way, each of which expresses one specific idea, are a convenient source material for constructing queries to the ontology in the SPARQL language. The analysis algorithm is based on the following sequence of basic steps: word tokenize, determining of marker words and phrases, identifying available type of proposition, identifying nouns groups, building a syntactic graph of a sentence, building semantic trees of propositions based on existing types of propositions, substituting parameters from semantic trees of propositions in the corresponding SPARQL query templates. The choice of an appropriate template depends on the type of proposition expressed by a given semantic tree of a proposition. The sets of concepts received as an answer are tied as corresponding answers to the previously defined semantic tree of proposition. In case of non-receipt of information from the ontology, the reduction of noun groups is carried out to express more general concepts and the building queries using them. This allows us to get some answer, although not as accurate as when we use the full noun group. The use of SPARQL query templates requires an a priori known ontology structure, which is also proposed in this paper. Such a system is applicable for dialogue using chat-bots or for automatically receiving answers to questions from the text.
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Korycinski, C., and Alan F. Newell. "Natural-language processing and automatic indexing." Indexer: The International Journal of Indexing: Volume 17, Issue 1 17, no. 1 (April 1, 1990): 21–29. http://dx.doi.org/10.3828/indexer.1990.17.1.8.
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The task of producing satisfactory indexes by automatic means has been tackled on two fronts: by statistical analysis of text and by attempting content analysis of the text in much the same way as a human indexcr does. Though statistical techniques have a lot to offer for free-text database systems, neither method has had much success with back-of-the-bopk indexing. This review examines some problems associated with the application of natural-language processing techniques to book texts.
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Rybinski, Maciej, Xiang Dai, Sonit Singh, Sarvnaz Karimi, and Anthony Nguyen. "Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis." JMIR Medical Informatics 9, no. 4 (April 30, 2021): e24020. http://dx.doi.org/10.2196/24020.
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Background The prognosis, diagnosis, and treatment of many genetic disorders and familial diseases significantly improve if the family history (FH) of a patient is known. Such information is often written in the free text of clinical notes. Objective The aim of this study is to develop automated methods that enable access to FH data through natural language processing. Methods We performed information extraction by using transformers to extract disease mentions from notes. We also experimented with rule-based methods for extracting family member (FM) information from text and coreference resolution techniques. We evaluated different transfer learning strategies to improve the annotation of diseases. We provided a thorough error analysis of the contributing factors that affect such information extraction systems. Results Our experiments showed that the combination of domain-adaptive pretraining and intermediate-task pretraining achieved an F1 score of 81.63% for the extraction of diseases and FMs from notes when it was tested on a public shared task data set from the National Natural Language Processing Clinical Challenges (N2C2), providing a statistically significant improvement over the baseline (P<.001). In comparison, in the 2019 N2C2/Open Health Natural Language Processing Shared Task, the median F1 score of all 17 participating teams was 76.59%. Conclusions Our approach, which leverages a state-of-the-art named entity recognition model for disease mention detection coupled with a hybrid method for FM mention detection, achieved an effectiveness that was close to that of the top 3 systems participating in the 2019 N2C2 FH extraction challenge, with only the top system convincingly outperforming our approach in terms of precision.
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P. P., Dr Joby. "Expedient Information Retrieval System for Web Pages Using the Natural Language Modeling." June 2020 2, no. 2 (June 1, 2020): 100–110. http://dx.doi.org/10.36548/jaicn.2020.2.003.
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Retrieving of information from the huge set of data flowing due to the day to day development in the technologies has become more popular as it assists in searching for the valuable information in a structured, unstructured or a semi structured data set like text, database, multimedia, documents, and internet etc. The retrieval of information is performed employing any one of the models starting from the simple Boolean model for retrieving information, or using other frame works such as probabilistic, vector space and the natural language modelling. The paper is emphasis on using a natural language model based information retrieval to recover the meaning insights from the enormous amount of data. The method proposed in the paper uses the latent semantic analysis to retrieve significant information’s from the question raised by the user or the bulk documents. The carried out method utilizes the fundamentals of semantic factor occurring in the data set to identify the useful insights. The experiment analysis of the proposed method is carried out with few state of art dataset such as TIME, LISA, CACM and the NPL etc. and the results obtained demonstrate the superiority of the method proposed in terms of precision, recall and F-score.
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Chen, Xieling, Ruoyao Ding, Kai Xu, Shan Wang, Tianyong Hao, and Yi Zhou. "A Bibliometric Review of Natural Language Processing Empowered Mobile Computing." Wireless Communications and Mobile Computing 2018 (June 28, 2018): 1–21. http://dx.doi.org/10.1155/2018/1827074.
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Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.
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Jamadi Khiabani, Parisa, Mohammad Ehsan Basiri, and Hamid Rastegari. "An improved evidence-based aggregation method for sentiment analysis." Journal of Information Science 46, no. 3 (March 18, 2019): 340–60. http://dx.doi.org/10.1177/0165551519837187.
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Sentiment analysis is one of the natural language processing tasks used to find reviews expressed in online texts and classify them into different classes. One of the most important factors affecting the efficiency of sentiment analysis methods is the aggregation algorithm used for scores combination. Recently, Dempster–Shafer algorithm has been used for scores aggregation. This algorithm has a higher precision than common methods such as average, weighed average, product and voting, but the problem with this algorithm is the aggregation of a dominant high or low score that is always selected by the algorithm as the overall score. In the current research, a new method is proposed for scores aggregation that employs both the most and the second probable classes to predict the final score. The proposed approach considers every review as a set of sentences each of which has its own sentiment orientation and score and computes the probability of belonging of every sentence to different classes in a five-star scale using a pure lexicon-based system. These probabilities are then used for document-level sentiment detection. To this aim, two-point structure is used to improve the Dempster–Shafer aggregation algorithm. The proposed method is applied to review datasets of TripAdvisor and CitySearch which have been used in previous studies. The obtained results show that in comparison with the original Dempster–Shafer aggregation method, the precision of the proposed method for both datasets is 23% and 27% higher, respectively.
Dissertations / Theses on the topic "Natural Language Information Analysis Method (NIAM)"
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Ravalli, Gilbert, and gravalli@swin edu au. "Translation of on object role model schema into the formal language Z." Swinburne University of Technology, 2005. http://adt.lib.swin.edu.au./public/adt-VSWT20060502.130326.
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In the development of information systems for business, structured approaches are widely used in practice. Structured approaches provide a prescription and guidelines for how to go about the process of developing an information system, are relatively easy to learn and provide tools which are well suited to their task. However, the products of structured approaches are sometimes seen to be vague and imprecise since requirements are written using natural language or represented in the form of models which do not have a formal foundation. This vagueness or ambiguity can be the source of problems later in development of the information system. A possible solution to this is to represent requirements using formal methods since these are seen as precise and unambiguous. However, formal methods are typically only a mathematical language for representing requirements. They are often regarded as difficult to learn and use. Even though formal methods of one sort or another have been in existence for many years they are not popular and appear unlikely to become popular in the future.
One possible approach to providing the advantages of structured approaches and formal methods is to provide translation procedures from the products of structured approaches to a formal description in a suitable formal language. The work in this thesis follows this theme and is aimed at the creation of a translation procedure from an Object Role Model (ORM) schema to a Z specification. An object role model schema is the end product of a process called the Natural Language Information Analysis Method (NIAM) which is used to produce an information model for an information system. NIAM is a method which has been used successfully in industry since the mid 1970s and continues to be used today.
This thesis provides a translation procedure from ORM to Z which is less arbitrary and more comprehensive than previous conversion procedures in the literature. It establishes a systematic method for
(i) choosing suitable types and variables for a Z specification and
(ii) predicates that express all the standard constraints available in ORM modelling.
The style of representation in Z preserves ORM�s concepts in a way that aids traceability and validation. The natural language basis of ORM, namely the use of elementary facts, is preserved. Furthermore, an ORM schema differentiates between abstract concepts and the means by which these concepts are represented symbolically and this thesis provides a representation in Z that maintains the distinction between conceptual objects and their symbolic representation. Identification schemes of entity types are also translated into the Z specification but it is left as an option in the translation procedure.
Guiding and evaluating the work conducted here are a published set of criteria for the evaluation of a conceptual schema. These have helped in making decisions regarding the translation procedure and for assessing my work and that of others.
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Jakob, Bremer. "Exploring Hybrid Topic Based Sentiment Analysis as Author Identification Method on Swedish Documents." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177893.
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The Swedish national bank has had shifting policies when it comes to publicity and confidentiality concerning publishing of texts within the bank. For some time, texts written by commissioners within the bank were decided to be published anonymously. Later they revoked the confidentiality policy, publishing all documents publicly again. This led to emerged interests in possible shifting attitudes toward topics discussed by the commissioners when writing anonymously versus publicly. On a request, based on the interests, there are ongoing analyses being conducted with the help of language technology where topics are extracted from the anonymous and public documents respectively. The aim is to find topics related to individual commissioners with the purpose of, as accurately as possible, identifying which of the anonymous documents is written by who. To discover unique relations between the commissioners and the generated topics, this thesis proposes hybrid topic based sentiment analysis as an author identification method to be able to use sentiments of topics as identifying features of commissioners. The results showed promise in the proposed approach. Though, further research is substantial, conducting comparisons with other acknowledged author identification methods, to confirm some level of efficacy, especially on documents containing close similarities among topics.
Book chapters on the topic "Natural Language Information Analysis Method (NIAM)"
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Romano, Maurizio, Francesco Mola, and Claudio Conversano. "Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction." In Proceedings e report, 147–51. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.29.
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The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction.
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Shelke, Nilesh M., and Shrinivas P. Deshpande. "Exploiting Chi Square Method for Sentiment Analysis of Product Reviews." In Natural Language Processing, 422–39. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch022.
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Sentiment analysis is an extension of data mining which employs natural language processing and information extraction task to recognize people's opinion towards entities such as products, services, issues, organizations, individuals, events, topics, and their attributes. It gives the summarized opinion of a writer or speaker. It has received lot of attention due to increasing number of posts/tweets on social sites. The proposed system is meant to classify a given text of review into positive, negative, or the neutral category. Primary objective of this article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews. Publicly available freely dictionary SentiWordNet 3.0 has been used for review classification. The proposed system is domain independent and context aware. Another objective of the proposed system is to identify the feature specific intensity with which reviewer has expressed his opinion. Effectiveness of the proposed system has been verified through performance matrix and compared with other research work.
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Selot, Smita, Neeta Tripathi, and A. S. Zadgaonkar. "Neural Network Model for Semantic Analysis of Sanskrit Text." In Natural Language Processing, 1011–25. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch049.
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Semantic analysis is the process of extracting meaning of the sentence, from a given language. From the perspective of computer processing, challenge lies in making computer understand the meaning of the given sentence. Understandability depends upon the grammar, syntactic and semantic representation of the language and methods employed for extracting these parameters. Semantics interpretation methods of natural language varies from language to language, as grammatical structure and morphological representation of one language may be different from another. One ancient Indian language, Sanskrit, has its own unique way of embedding syntactic information within words of relevance in a sentence. Sanskrit grammar is defined in 4000 rules by PaninI reveals the mechanism of adding suffixes to words according to its use in sentence. Through this article, a method of extracting meaningful information through suffixes and classifying the word into a defined semantic category is presented. The application of NN-based classification has improved the processing of text.
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McCarthy, Philip M., David Dufty, Christian F. Hempelmann, Zhiqiang Cai, Danielle S. McNamara, and Arthur C. Graesser. "Newness and Givenness of Information." In Applied Natural Language Processing, 457–78. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-741-8.ch027.
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The identification of new versus given information within a text has been frequently investigated by researchers of language and discourse. Despite theoretical advances, an accurate computational method for assessing the degree to which a text contains new versus given information has not previously been implemented. This study discusses a variety of computational new/given systems and analyzes four typical expository and narrative texts against a widely accepted theory of new/given proposed by Prince (1981). Our findings suggest that a latent semantic analysis (LSA) based measure called span outperforms standard LSA in detecting both new and given information in text. Further, span outperforms standard LSA for distinguishing low versus high cohesion versions of text. Our results suggest that span may be a useful variable in a wide array of discourse analyses.
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Ansari, Gunjan, Shilpi Gupta, and Niraj Singhal. "Natural Language Processing in Online Reviews." In Advances in Business Information Systems and Analytics, 40–64. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4240-8.ch003.
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The analysis of the online data posted on various e-commerce sites is required to improve consumer experience and thus enhance global business. The increase in the volume of social media content in the recent years led to the problem of overfitting in review classification. Thus, there arises a need to select relevant features to reduce computational cost and improve classifier performance. This chapter investigates various statistical feature selection methods that are time efficient but result in selection of few redundant features. To overcome this issue, wrapper methods such as sequential feature selection (SFS) and recursive feature elimination (RFE) are employed for selection of optimal feature set. The empirical analysis was conducted on movie review dataset using three different classifiers and the results depict that SVM could achieve f-measure of 96% with only 8% selected features using RFE method.
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Mishra, Vinod Kumar, and Himanshu Tiruwa. "Aspect-Based Sentiment Analysis of Online Product Reviews." In Advances in Business Information Systems and Analytics, 175–91. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2031-3.ch010.
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Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion from text. It is also considered as a task of natural language processing and data mining. Sentiment analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect based sentiment analysis with current and future trend of research on aspect based sentiment analysis. This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600. To perform aspect based classification we are using lexical approach on eclipse platform which classify the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for part of speech tagging, frequency based method is used for extraction of the aspects/features and used negation handling for improving the accuracy of the system.
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Şahin, Durmuş Özkan, and Erdal Kılıç. "An Extensive Text Mining Study for the Turkish Language." In Advances in Business Information Systems and Analytics, 272–306. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4240-8.ch012.
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In this study, the authors give both theoretical and experimental information about text mining, which is one of the natural language processing topics. Three different text mining problems such as news classification, sentiment analysis, and author recognition are discussed for Turkish. They aim to reduce the running time and increase the performance of machine learning algorithms. Four different machine learning algorithms and two different feature selection metrics are used to solve these text classification problems. Classification algorithms are random forest (RF), logistic regression (LR), naive bayes (NB), and sequential minimal optimization (SMO). Chi-square and information gain metrics are used as the feature selection method. The highest classification performance achieved in this study is 0.895 according to the F-measure metric. This result is obtained by using the SMO classifier and information gain metric for news classification. This study is important in terms of comparing the performances of classification algorithms and feature selection methods.
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Atkins, Clare. "INTECoM." In Developing Quality Complex Database Systems, 240–60. IGI Global, 2001. http://dx.doi.org/10.4018/978-1-878289-88-9.ch010.
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An important contributor to the success of any complex database development is the comprehensive and accurate capture and recording of the users’ information requirements. Indeed, both the technical and economic success of the system under development is likely to rest largely on the quality of the data structure design and the information requirement analysis on which it is based. The data models, which represent the results of the analysis and design activities necessary to achieve this quality outcome, are therefore critical components of the database development process. Nevertheless, research suggests that this modeling is not always done well and in some cases is not done at all (e.g., Hitchman, 1995). However, implicit in the creation of a database is the design of a data model, and thus the only optional feature is the level of formality that has been followed in its development (Simsion, 1994). Since the publication of Chen’s (1976) original description of an Entity-relationship (E-R) model, a significant amount of academic research into data modeling has concentrated on providing ever richer, more complex and more formal models with which to better represent reality (Hirschheim, Klein & Lyytinen, 1995). In addition, researchers and practitioners have also recognized the importance of data models as a means of communication. However, little attention has been given to examining the appropriateness of various modeling techniques to the very different requirements of the analysis and design activities that they support, although matching tools to activities would seem to be an essential prerequisite for success. The INTECoM framework, described in this chapter, was developed to emphasize and better serve the differing nature of these activities, and also to improve access for all users to both the process and the outcome of data modeling. The framework was initially instantiated with two widely used data modeling techniques, the NIAM-CSDP (Natural Language Information Analysis-Conceptual Schema Design Procedure) and the Entity-Relationship (E-R) approach. This instantiation was chosen primarily because the two techniques represent significantly different ways of working (Bronts, Brouwer, Martens & Proper, 1995) towards the construction of a relational database. This is not to suggest that other instantiations are not possible or desirable, particularly where the target DBMS is of a different paradigm.
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Chawla, Suruchi. "Application of Deep Learning Model Convolution Neural Network for Effective Web Information Retrieval." In Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, 100–120. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3299-7.ch007.
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Convolution neural network (CNN) is the most popular deep learning method that has been used for various applications like image recognition, computer vision, and natural language processing. In this chapter, application of CNN in web query session mining for effective information retrieval is explained. CNN has been used for document analysis to capture the rich contextual structure in a search query or document content. The document content represented in matrix form using Word2Vec is applied to CNN for convolution as well as maxpooling operations to generate the fixed length document feature vector. This fixed length document feature vector is input to fully connected neural network (FNN) and generates the semantic document vector. These semantic document vectors are clustered to group similar document for effective web information retrieval. An experiment was performed on the data set of web query sessions, and results confirm the effectiveness of CNN in web query session mining for effective information retrieval.
Conference papers on the topic "Natural Language Information Analysis Method (NIAM)"
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Liu, Rui, Jeremy Webb, and Xiaoli Zhang. "Natural-Language-Instructed Industrial Task Execution." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60063.
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To effectively cooperate with a human, advanced manufacturing machines are expected to execute the industrial tasks according to human natural language (NL) instructions. However, NL instructions are not explicit enough to be understood and are not complete enough to be executed, leading to incorrected executions or even execution failure. To address these problems for better execution performance, we developed a Natural-Language-Instructed Task Execution (NL-Exe) method. In NL-Exe, semantic analysis is adopted to extract task-related knowledge, based on what human NL instructions are accurately understood. In addition, logic modeling is conducted to search the missing execution-related specifications, with which incomplete human instructions are repaired. By orally instructing a humanoid robot Baxter to perform industrial tasks “drill a hole” and “clean a spot”, we proved that NL-Exe could enable an advanced manufacturing machine to accurately understand human instructions, improving machine’s performance in industrial task execution.
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Lash, Alex, Kevin Murray, and Gregory Mocko. "Natural Language Processing Applications in Requirements Engineering." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71084.
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In the design process, the requirements serve as the benchmark for the entire product. Therefore, the quality of requirement statements is essential to the success of a design. Because of their ergonomic-nature, most requirements are written in natural language (NL). However, writing requirements in natural language presents many issues such as ambiguity, specification issues, and incompleteness. Therefore, identifying issues in requirements involves analyzing these NL statements. This paper presents a linguistic approach to requirement analysis, which utilizes grammatical elements of requirements statements to identify requirement statement issues. These issues are organized by the entity—word, sentence, or document—that they affect. The field of natural language processing (NLP) provides a core set of tools that can aid with this linguistic analysis and provide a method to create a requirement analysis support tool. NLP addresses requirements on processing levels: lexical, syntactic, semantic, and pragmatic. While processing on the lexical and syntactic level are well-defined, mining semantic and pragmatic data is performed in a number of different methods. This paper provides an overview of these current requirement analysis methods in light of the presented linguistic approach. This overview will be used to identify areas for further research and development. Finally, a prototype requirement analysis support tool will be presented. This tool seeks to demonstrate how the semantic processing level can begin to be addressed in requirement analysis. The tool will analyze a sample set of requirements from a family of military tactical vehicles (FMTV) requirements document. It implements NLP tools to semantically compare requirements statements based upon their grammatical subject.
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Neustein, Amy. "SEQUENCE PACKAGE ANALYSIS: A New Natural Language Understanding Method for Intelligent Mining of Recordings of Doctor-Patient Interviews and Health-Related Blogs." In 2007 4th International Conference on Information Technology New Generations. IEEE, 2007. http://dx.doi.org/10.1109/itng.2007.179.
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Nordgren, Andreas, and Hideki Aoyama. "Style Design Method Based on Form Impressions." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84955.
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The automotive industry is very competitive and companies are spending enormous amounts of resources on the development of new cars. The success of a new model is highly correlated to how well the designers and engineers have been able to blend features, functionality, quality and design to bring an attractive car to a certain segment of the market at the right time. Furthermore, as modern manufacturing techniques have enabled most manufacturers to offer standard features in their cars, the design has become a major selling point and one of the key factors for the ‘image’ associated with a company. However, the image, or form impression of a car, stated in natural language, is subtle and difficult to directly relate to concrete design parameters. With few tools to address this issue, designers are left to rely on their experience and sensitivity to current trends in order to meet the customer expectations for a new model. The purpose of the method reported in this paper is to provide a foundation for a design support system, which can help designers visualize and validate the complex relationship between form impressions and design parameters. This was achieved by expressing form impressions in natural language as sets of 10 weighted attributes. 14 design parameters were established to describe the basic shape of a car and data on the form impression for 31 different shapes was collected via a survey designed by the Taguchi method. Factor analysis was performed to extract correlated factors and eliminate the overlap of meaning between attributes. A neural network, able to relate form impressions expressed in these factors to basic proportions of a car, was created, trained and used to generalize design parameters corresponding to any form impression presented to it. Finally, a 3D-model with the desired form impression was automatically created by the CAD-system outlined in this paper. These results show that this method can be used to create a design support system, which has a sensibility to the form impressions various shapes will give.
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Yi, Michael, Pradeepkumar Ashok, Dawson Ramos, Taylor Thetford, Spencer Bohlander, James Moisan, and Michael Behounek. "Natural Language Processing Applied to Reduction of False and Missed Alarms in Kick and Lost Circulation Detection." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206340-ms.
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Abstract Kick and lost circulation events are large contributors to non-productive time. Therefore, early detection of these events is crucial. In the absence of good flow in and flow out sensors, pit volume trends offer the best possibility for influx/loss detection, but errors occur since external mud addition /removal to the pits is not monitored or sensed. The goal is to reduce false alarms caused by such mud additions and removal. Data analyzed from over 100s of wells in North America show that mud addition and removal results in certain unique pit volume gain / loss trends, and these trends are quite different from a kick, a lost circulation or a wellbore breathing event trend. Additionally, driller's input text memos into the data aggregation system (EDR) and these memos often provide information with regards to pit operations. In this paper, we introduce a method that utilizes a Bayesian network to aggregate trends detected in time-series data with events identified by natural language processing (NLP) of driller memos critical to greatly improve the accuracy and robustness of kick and lost circulation detection. The methodology was implemented in software that is currently running on rigs in North America. During the test phase, we applied it on several historical wells with lost circulation events and several historical wells with kick events. We were able to identify and quantify the losses even during connections and mud additions, where usually pit volume was increasing despite continual losses. Also, the real-time simultaneous analysis of driller memos provides context to pit volume trends and further reduce the false alarms. The algorithm is also able to take account of pit volume that was reduced due to drilling. Quantification of the losses offers more insight into what lost circulation material to use and the changes in the rate of loss while drilling. This approach was very robust in discovering kicks as well and differentiating it from mud removal and wellbore breathing events. These historical case studies will be detailed in this paper. This is the first time that patterns in mud volume addition and removal detected from time-series data have been used along with driller memos using NLP to reduce false alerts in kick and lost circulation detection. This approach is particularly useful in identifying kick and lost circulation events from pit volume data, especially when good flow in and flow out sensors are not available. The paper provides guidance on how real-time sensor data can be combined with textual data to improve the outputs from an advisory system.
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Sen, Chiradeep, Alolika Mukhopadhyay, John Fields, and Farhad Ameri. "An Approach for Measuring Information Content of Textual Engineering Requirements Using a Form-Neutral Representation." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34438.
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This paper demonstrates a generic approach for measuring the information content of artifacts produced and used in early stages of mechanical design. Engineering design requirements are selected for information content analysis for illustration. In this method, requirements in natural language are translated to an Entity-Relation-Attribute-Value (ERAV) model composed of well-defined elements. A protocol for this translation is proposed and validated. Four different metrics, based on raw element count, count weighted by arbitrary ordinal scale, count weighted by node cardinality, and Shannon’s entropy are then applied to the ERAV model for measuring information content. The method proposed is generic enough to be applied to most design documents that use natural language as the knowledge representation formalism.
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Song, Binyang, Emmett Meinzer, Akash Agrawal, and Christopher McComb. "Topic Modeling and Sentiment Analysis of Social Media Data to Drive Experiential Redesign." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22567.
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Abstract The elicitation of customer pain points is a crucial early step in the design or redesign of successful products and services. Online, user-generated data contains rich, real-time information about customer experience, requirements, and preferences. However, it is a nontrivial task to retrieve useful information from these sources because of the sheer amount of data, often unstructured. In this work, we build on previous efforts that used natural language processing techniques to extract meaning from online data and facilitate experiential redesign and extend them by integrating a sentiment analysis. As a use case, we explore the airline industry. A considerable portion of potential passengers opt out of traveling by airplane due to aviophobia, a fear of flying. This causes a market loss to the industry and inconvenience for those who experience aviophobia. The potential contributors to aviophobia are complex and diverse, involving physical, psychological and emotional reactions to the air travel experience. A methodology that is capable of accommodating the complexity and diversity of the commercial airline industry user-generated data is necessary to effectively mine customer pain points. To address the demand, we propose a novel methodology in this study. Using passenger commentary data posted on Reddit, the method implements topic modeling to extract common themes from the commentaries and employs sentiment analysis to elicit and interpret the salient information contained in the extracted themes. This paper ends by providing specific recommendations that are germane to the use case as well as suggesting future research directions.
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Sabbagh, Ramin, and Farhad Ameri. "Supplier Clustering Based on Unstructured Manufacturing Capability Data." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85865.
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The descriptions of capabilities of manufacturing companies can be found in multiple locations including company websites, legacy system databases, and ad hoc documents and spreadsheets. The capability descriptions are often represented using natural language. To unlock the value of unstructured capability information and learn from it, there is a need for developing advanced quantitative methods supported by machine learning and natural language processing techniques. This research proposes a multi-step unsupervised learning methodology using K-means clustering and topic modeling techniques in order to build clusters of suppliers based on their capabilities, extract and organize the manufacturing capability terminology, and discover nontrivial patterns in manufacturing capability corpora. The capability data is extracted either directly from the website of manufacturing firms or from their profiles in e-sourcing portals and directories. Feature extraction and dimensionality reduction process in this work in supported by Ngram extraction and Latent Semantic Analysis (LSA) methods. The proposed clustering method is validated experimentally based a dataset composed of 150 capability descriptions collected from web-based sourcing directories such as the Thomas Net directory for manufacturing companies. The results of the experiment show that the proposed method creates supplier cluster with high accuracy.
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Shi, Feng, Liuqing Chen, Ji Han, and Peter Childs. "Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67230.
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With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.
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Camburn, Bradley, Yuejun He, Sujithra Raviselvam, Jianxi Luo, and Kristin Wood. "Evaluating Crowdsourced Design Concepts With Machine Learning." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97285.
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Abstract Automation has enabled design of increasingly complex products, services, and systems. Advanced technology enables designers to automate repetitive tasks in earlier design phases, even high level conceptual ideation. One particularly repetitive task in ideation is to process the large concept sets that can be developed through crowdsourcing. This paper introduces a method for filtering, categorizing, and rating large sets of design concepts. It leverages unsupervised machine learning (ML) trained on open source databases. Input design concepts are written in natural language. The concepts are not pre-tagged, structured or processed in any way which requires human intervention. Nor does the approach require dedicated training on a sample set of designs. Concepts are assessed at the sentence level via a mixture of named entity tagging (keywords) through contextual sense recognition and topic tagging (sentence topic) through probabilistic mapping to a knowledge graph. The method also includes a filtering strategy, the introduction of two metrics, and a selection strategy for assessing design concepts. The metrics are analogous to the design creativity metrics novelty, level of detail, and a selection strategy. To test the method, four ideation cases were studied; over 4,000 concepts were generated and evaluated. Analyses include: asymptotic convergence analysis; a predictive industry case study; and a dominance test between several approaches to selection of high ranking concepts. Notably, in a series of binary comparisons between concepts that were selected from the entire set by a time limited human versus those with the highest ML metric scores, the ML selected concepts were dominant.