Relevant bibliographies by topics / Linguistic Extraction

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Linguistic Extraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Linguistic Extraction"

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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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Rodzuan, Nur Aniq Syafiq, Shahreen Kasim, Mohanavali Sithambranathan, and Muhammad Zaki Hassan. "Classification of Biomedical Literature in Hypertension and Diabetes." International Journal on Data Science 1, no. 2 (2020): 114–19. http://dx.doi.org/10.18517/ijods.1.2.114-119.2020.

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Textual information gives us more clear information as it is presented using words and characters, which is easy for humans to understand. To extract this kind of information, text mining was introduced as new technology. Text mining is the process of extracting non-trivial patterns or knowledge from text documents or from textual databases. The purpose of this research paper is to perform and compare keyword extraction using statistical and linguistic extraction tools for 120 text documents related to hypertension and diabetes disease. In order to draw this comparison, RStudio, a statistical-
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Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630003. http://dx.doi.org/10.1142/s2425038416300032.

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Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation typ
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Hukari, Thomas E., and Robert D. Levine. "Adjunct extraction." Journal of Linguistics 31, no. 2 (1995): 195–226. http://dx.doi.org/10.1017/s0022226700015590.

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In current linguistic theory, the theoretical status of adjunct extractions, as in for example How often do you think Robin sees Kim? is, somewhat surprisingly, an unresolved issue, with some investigators arguing that only arguments extract syntactically, entailing analyses of adverbial gaps via fundamentally different mechanisms from those posited for argument extraction. We adduce extensive evidence against such positions from a number of languages which exhibit morphological or syntactic phenomena which are sensitive to binding (extraction) domains and where this morphosyntactic flagging i
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Szymanski, Terrence. "Automatic Extraction of Linguistic Data from Digitized Documents." Annual Meeting of the Berkeley Linguistics Society 39, no. 1 (2013): 273. http://dx.doi.org/10.3765/bls.v39i1.3886.

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In lieu of an abstract, here is a brief excerpt:This paper presents a system for automatically extracting linguistic data from digitized linguistic documents using a combination of existing software packages and custom scripts. The system is designed to leverage existing resources in online digital libraries in order to bootstrap the creation of large, multi-lingual linguistic corpora, which can then be used to conduct data-driven experimental research into cross-linguistic or universal linguistic phenomena. The system identifies instances of foreign-language text accompanied by reference-lang
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Khairova, Nina, Orken Mamyrbayev, Kuralay Mukhsina, Anastasiia Kolesnyk, and Saurabh Pratap. "Logical-linguistic model for multilingual Open Information Extraction." Cogent Engineering 7, no. 1 (2020): 1714829. http://dx.doi.org/10.1080/23311916.2020.1714829.

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Benamara, Farah, Maite Taboada, and Yannick Mathieu. "Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications." Computational Linguistics 43, no. 1 (2017): 201–64. http://dx.doi.org/10.1162/coli_a_00278.

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The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the field exist. Most surveys, however, do not bring the two disciplines together to show how methods from linguistics can benefit computational sentiment analysis systems. In this survey, we show how incorporating linguistic insights, discourse information, and other contextual phenomena, in combination with the statistical exploitation of data, can result in
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Solovyev, Valery, and Vladimir Ivanov. "Knowledge-Driven Event Extraction in Russian: Corpus-Based Linguistic Resources." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4183760.

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Automatic event extraction form text is an important step in knowledge acquisition and knowledge base population. Manual work in development of extraction system is indispensable either in corpus annotation or in vocabularies and pattern creation for a knowledge-based system. Recent works have been focused on adaptation of existing system (for extraction from English texts) to new domains. Event extraction in other languages was not studied due to the lack of resources and algorithms necessary for natural language processing. In this paper we define a set of linguistic resources that are neces
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Fulford, Heather. "Exploring terms and their linguistic environment in text." Terminology 7, no. 2 (2001): 259–79. http://dx.doi.org/10.1075/term.7.2.08ful.

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The proliferation of specialist texts over recent decades has exacerbated the need for term extraction software to assist terminologists in compiling terminology collections. To this end, an automated approach to English term extraction is presented, which, in keeping with the multidisciplinary working environments of many contemporary terminologists, is designed to be domain independent. Based on observations made of the linguistic features of terms and their linguistic environment in text, this approach identifies single- and multi-word terms spanning a range of word classes. An implementati
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Gu, Jinghang, Longhua Qian, and Guodong Zhou. "Chemical-induced disease relation extraction with various linguistic features." Database 2016 (2016): baw042. http://dx.doi.org/10.1093/database/baw042.

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Dissertations / Theses on the topic "Linguistic Extraction"

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Mason, Oliver Jan. "The automatic extraction of linguistic information from text corpora." Thesis, University of Birmingham, 2006. http://etheses.bham.ac.uk//id/eprint/116/.

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This is a study exploring the feasibility of a fully automated analysis of linguistic data. It identifies a requirement for large-scale investigations, which cannot be done manually by a human researcher. Instead, methods from natural language processing are suggested as a way to analyse large amounts of corpus data without any human intervention. Human involvement hinders scalability and introduces a bias which prevents studies from being completely replicable. The fundamental assumption underlying this work is that linguistic analysis must be empirical, and that reliance on existing theories
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Nepal, Srijan. "Linguistic Approach to Information Extraction and Sentiment Analysis on Twitter." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342544962.

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Shahid, Ahmad. "Extraction of linguistic resources from multilingual corpora and their exploitation." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/2111/.

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Increasing availability of on-line and off-line multilingual resources along with the developments in the related automatic tools that can process this information, such as GIZA++ (Och & Ney 2003), has made it possible to build new multilingual resources that can be used for NLP/IR tasks. Lexicon generation is one such task, which if done by hand is quite expensive with human and capital costs involved. Generation of multilingual lexicons can now be automated, as is done in this research work. Wikipedia, an on-line multilingual resource was gainfully employed to automatically build multilingua
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Pettersson, Eva. "Spelling Normalisation and Linguistic Analysis of Historical Text for Information Extraction." Doctoral thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269753.

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Historical text constitutes a rich source of information for historians and other researchers in humanities. Many texts are however not available in an electronic format, and even if they are, there is a lack of NLP tools designed to handle historical text. In my thesis, I aim to provide a generic workflow for automatic linguistic analysis and information extraction from historical text, with spelling normalisation as a core component in the pipeline. In the spelling normalisation step, the historical input text is automatically normalised to a more modern spelling, enabling the use of existin
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Lindes, Peter. "OntoSoar: Using Language to Find Genealogy Facts." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4133.

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There is a need to have an automated system that can read family history books or other historical texts and extract as many genealogy facts as possible from them. Embley and others have applied traditional information extraction techniques to this problem in a system called OntoES with a reasonable amount of success. In parallel much linguistic theory has been developed in the past decades, and Lonsdale and others have built computational embodiments of some of these theories using Soar. In this thesis we introduce a system called OntoSoar which combines the Link Grammar Parser using a gramma
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Martínez, de la Mora Daniela 1983. "The Universality of perceptual and linguistic constraints in the extraction of rule-like patterns : a cross-species comparison." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/113604.

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Studies have shown that linguistic and perceptual constraints are important for speech processing. First, rule-like structures are more easily learned over vowels than over consonants. Second, sequences varying in pitch and duration are grouped following the Iambic – Trochaic Law (ITL). In this research, I investigated the origins of these linguistic and perceptual constraints. My aim was to test if vowels’ acoustic saliency was the reason why they are the preferred target for abstract computations, and to explore the extent to which the principles of the ITL come from evolutionary her
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Danilova, Vera. "Linguistic support for protest event data collection." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/374232.

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sta tesis aborda el problema de la cualidad de recopilación automática de datos sobre protestas y propone herramientas de extracción multilíngüe de atributos del evento de protesta para mejorar la calidad de la unidad de análisis. El trabajo incluye la exploración del estado de arte en los dominios de la recopilación automática de datos sobre protestas y la extracción multilíngüe de eventos. En la ausencia de una colección de datos multilíngües sobre protestas anotados por expertos para el aprendizaje supervisado nos enfocamos en el tratamiento de noticias multilíngües basado en patrones lin
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Marcińczuk, Michał. "Pattern Acquisition Methods for Information Extraction Systems." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4291.

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This master thesis treats about Event Recognition in the reports of Polish stockholders. Event Recognition is one of the Information Extraction tasks. This thesis provides a comparison of two approaches to Event Recognition: manual and automatic. In the manual approach regular expressions are used. Regular expressions are used as a baseline for the automatic approach. In the automatic approach three Machine Learning methods were applied. In the initial experiment the Decision Trees, naive Bayes and Memory Based Learning methods are compared. A modification of the standard Memory Based Learning
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Aslam, Irfan. "Semantic frame based automatic extraction of typological information from descriptive grammars." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17893.

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This thesis project addresses the machine learning (ML) modelling aspects of the problem of automatically extracting typological linguistic information of natural languages spoken in South Asia from annotated descriptive grammars. Without getting stuck into the theory and methods of Natural Language Processing (NLP), the focus has been to develop and test a machine learning (ML) model dedicated to the information extraction part. Starting with the existing state-of-the-art frameworks to get labelled training data through the structured representation of the descriptive grammars, the problem ha
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Oudni, Amal. "Fouille de données par extraction de motifs graduels : contextualisation et enrichissement." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066437/document.

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Les travaux de cette thèse s'inscrivent dans le cadre de l'extraction de connaissances et de la fouille de données appliquée à des bases de données numériques ou floues afin d'extraire des résumés linguistiques sous la forme de motifs graduels exprimant des corrélations de co-variations des valeurs des attributs, de la forme « plus la température augmente, plus la pression augmente ». Notre objectif est de les contextualiser et de les enrichir en proposant différents types de compléments d'information afin d'augmenter leur qualité et leur apporter une meilleure interprétation. Nous proposons q
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Books on the topic "Linguistic Extraction"

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Three investigations of extraction. MIT Press, 1998.

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Xu, Fei-Yu. Bootstrapping relation extraction from semantic seeds. German Research Center for Artificial Intelligence, 2008.

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Multiple Wh-fronting. John Benjamins Pub., 2003.

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Peter, Jackson. Natural language processing for online applications: Text retrieval, extraction and categorization. 2nd ed. John Benjamins Pub. Company, 2007.

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Müller, Sonja. (Un)informativität und Grammatik: Extraktion aus Nebensätzen im Deutschen. Stauffenburg, 2011.

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Paliouras, Georgios. Knowledge-Driven Multimedia Information Extraction and Ontology Evolution: Bridging the Semantic Gap. Springer Berlin Heidelberg, 2011.

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Movement in language: Interactions and architectures. Oxford University Press, 2001.

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Sabourin, Conrad. Optical character recognition and document segmentation: Character preprocessing, thinning, isolation, segmentation, feature extraction, cursive and multi-font recognition, writer/scriptor identification : bibliography. Infolingua, 1994.

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The in-situ approach to sluicing. John Benjamins Publishing Company, 2015.

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Sabourin, Conrad. Computational linguistics in information science: Information retrieval (full-text or conceptual), automatic indexing, text abstraction, content analysis, information extraction, query languages : bibliography. Infolingua, 1994.

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Book chapters on the topic "Linguistic Extraction"

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Cardeñosa, Jesús, Miguel Ángel de la Villa, and Carolina Gallardo. "Linguistic Patterns for Encyclopaedic Information Extraction." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40769-7_57.

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Ji, Heng, and Ralph Grishman. "Collaborative entity extraction and translation." In Current Issues in Linguistic Theory. John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/cilt.309.06ji.

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Martín, Juan. "On the extraction from NPs in Spanish." In Linguistic Perspectives on Romance Languages. John Benjamins Publishing Company, 1993. http://dx.doi.org/10.1075/cilt.103.29mar.

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Kim, Jin-Dong, Tomoko Ohta, and Jun’ichi Tsujii. "Multilevel Annotation for Information Extraction." In Linguistic Modeling of Information and Markup Languages. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3331-4_7.

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Müller, Gereon, and Wolfgang Sternefeld. "Extraction, lexical variation, and the theory of Barriers." In Current Issues in Linguistic Theory. John Benjamins Publishing Company, 1995. http://dx.doi.org/10.1075/cilt.114.04mul.

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Duffield, Nigel. "Conspicuous Absences: Mutation & Extraction in Negated Contexts." In Studies in Natural Language and Linguistic Theory. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0155-4_3.

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Virk, Shafqat Mumtaz, Lars Borin, Anju Saxena, and Harald Hammarström. "Automatic Extraction of Typological Linguistic Features from Descriptive Grammars." In Text, Speech, and Dialogue. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64206-2_13.

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Agrawal, Rakesh, Howard Ho, François Jacquenet, and Marielle Jacquenet. "Mining Information Extraction Rules from Datasheets Without Linguistic Parsing." In Innovations in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11504894_69.

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Ishibuchi, Hisao, Tomoharu Nakashima, and Tadahiko Murata. "Multiobjective Optimization in Linguistic Rule Extraction from Numerical Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44719-9_41.

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Presutti, Valentina, Francesco Draicchio, and Aldo Gangemi. "Knowledge Extraction Based on Discourse Representation Theory and Linguistic Frames." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33876-2_12.

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Conference papers on the topic "Linguistic Extraction"

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Kim, Sanghee, Rob H. Bracewell, and Ken M. Wallace. "A Framework for Automatic Causality Extraction Using Semantic Similarity." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35193.

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Textual documents are the most common way of storing and distributing information within organizations. Extracting useful information from large text collections is therefore the goal of every organization that would like to take advantage of the experience encapsulated in those texts. Entering data using a free text style is easy, as it does not require any special training. However, unstructured texts pose a major challenge for automatic extraction and retrieval systems. Generally, deep levels of text analysis using advanced and complex linguistic processing are necessary that involve comput
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Hu, Xinghua, and Bin Wu. "Automatic Keyword Extraction Using Linguistic Features." In 2006 6th IEEE International Conference on Data Mining Workshops. IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.36.

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Li, Jie, Yanhua Yu, and Yunjie Ji. "Linguistic attention-based model for aspect extraction." In 2018 International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2018. http://dx.doi.org/10.1117/12.2513845.

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Shirai, K., and T. Hamada. "Linguistic knowledge extraction from real language behavior." In the 11th coference. Association for Computational Linguistics, 1986. http://dx.doi.org/10.3115/991365.991439.

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Moschitti, Alessandro, and Silvia Quarteroni. "Kernels on linguistic structures for answer extraction." In the 46th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1557690.1557720.

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Behin-Faraz, Hossein, Peyman Passban, and Mahmoud Shokrollahi-Far. "A reliable linguistic filter for Farsi term extraction." In 2013 5th Conference on Information and Knowledge Technology (IKT). IEEE, 2013. http://dx.doi.org/10.1109/ikt.2013.6620088.

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Sahmoudi, Issam, and Abdelmonaime Lachkar. "Towards a linguistic patterns for arabic keyphrases extraction." In 2016 International Conference on Information Technology for Organizations Development (IT4OD). IEEE, 2016. http://dx.doi.org/10.1109/it4od.2016.7479308.

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Baturone, Iluminada, and Andres Gersnoviez. "Automatic extraction of linguistic models for image description." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584798.

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Hulth, Anette. "Improved automatic keyword extraction given more linguistic knowledge." In the 2003 conference. Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1119355.1119383.

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Angeli, Gabor, Melvin Jose Johnson Premkumar, and Christopher D. Manning. "Leveraging Linguistic Structure For Open Domain Information Extraction." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-1034.

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Reports on the topic "Linguistic Extraction"

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Chew, Peter A. Identification of threats using linguistics-based knowledge extraction. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/940522.

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