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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1...50 51 52 53 54 Hits 1.061 – 1.080 of 1.080 1061 Dynamic Integration of Multiple Evidence Sources for Ontology Learning Gerhard Wohlgenannt; Albert Weichselbraun; Arno Scharl... In: http://eprints.weblyzard.com//56/1/wohlgenannt2012a-dynamicIntegrationOfMultipleEvidenceSources.pdf BASE Show details 1062 An Innovative Two-Stage WSD Unsupervised Method Un Innovador Método No Supervisado para Desambiguación de Sentidos de Palabras basado en dos etapas Javier Tejada Cárcamo; Sociedad Peruana De Computación; Er Gelbukh... In: http://www.sepln.org/revistaSEPLN/revista/40/15p20.pdf BASE Show details 1063 Extractive Summarisation of Legal Texts Ben Hachey; Claire Grover In: http://www.ltg.ed.ac.uk/np/publications/ltg/papers/Hachey2006Extractive.pdf BASE Show details 1064 EDUCATING LIA: THE DEVELOPMENT OF A LINGUISTICALLY ACCURATE MEMORY-BASED LEMMATISER FOR AFRIKAANS Hendrik J. Groenewald In: http://dl.ifip.org/db/conf/ifip12/iip2006/Groenewald06.pdf BASE Show details 1065 Distributed Representations for Semantic Matching in non-factoid Question Answering Piero Molino; Luca Maria Aiello In: http://ceur-ws.org/Vol-1204/papers/paper_9.pdf BASE Show details 1066 Tutor Dialogue Planning with Contextual Information and Discourse Structure Robert Fisher; Reid Simmons In: http://www.cs.cmu.edu/afs/cs/user/rwfisher/www/Curriculum_Vitae_files/fisher_simmons_its14.pdf BASE Show details 1067 ONTOLOGY SWITCHING FOR THE SOCIAL SCIENCES. METHODS FOR THE UNDERLYING CORPUS ANALYSIS Robert Strötgen In: http://www.stroetgen.de/Dokumente/rc33-2004.pdf BASE Show details 1068 A Proposed Architecture for Automated Assessment of Use Case Diagrams Vinay Vachharajani; Jyoti Pareek Ph. D In: http://research.ijcaonline.org/volume108/number4/pxc3900193.pdf BASE Show details 1069 THE EFFECT OF PARALLEL CORPUS QUALITY VS SIZE IN ENGLISH-TO- TURKISH SMT Eray Yıldızahmed; Cüneyd Tantuğand; Banu Diri... In: http://airccj.org/CSCP/vol4/csit42503.pdf BASE Show details 1070 Experience Nitin Madnani In: http://www.desilinguist.org/pdf/madnani-cv.pdf BASE Show details 1071 CALICO Journal, 26(3) Diane M. Napolitano; A Stent In: http://www.research.att.com/people/Stent_Amanda_J/library/publications/napolitanostent_09.pdf BASE Show details 1072 Taxonomy and Evaluation of Markers for Computational Foaad Khosmood; Robert Levinson In: http://world-comp.org/p2011/ICA3763.pdf BASE Show details 1073 Gradient for Structured Prediction Porus Patell In: http://www.cs.sfu.ca/%7Eanoop/students/porus_patell/poruspatell-mscProject.pdf BASE Show details 1074 Social Network- An autonomous system designed for radio recommendation. Grzegorz Dziczkowski; Rue Saint Honore; Lamine Bougueroua In: http://www.cri.ensmp.fr/classement/doc/A-410.pdf BASE Show details 1075 A Maximum Entropy (ME) Based Translation Model for Chinese Characters Conversion Fai Wong; Sam Chao; Cheong Cheong Hao... In: http://www.cicling.org/2009/RCS-41/267-276.pdf BASE Show details 1076 Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification Dhiraj Gurkhe; Rishit Bhatia In: http://research.ijcaonline.org/volume99/number13/pxc3898274.pdf Abstract: Effective Sentiment Analysis Of Social Media Datasets Using Naive Bayesian Classification involves extraction of subjective in-formation from textual data. A normal human can easily understand the sentiment of a document written in natural language based on its knowledge of understanding the polarity of words (unigram, bi-gram and n-grams) and in some cases the general semantics used to describe the subject. The project aims to make the machine extract the polarity (positive, negative or neutral) of social media dataset with respect to the queried keyword. This project introduces an ap-proach for automatically classifying the sentiment of social media data by using the following procedure: First the training data is fed to the Sentiment Analysis Engine for learning by using machine learning algorithm.After the learning is complete with qualified ac-curacy, the machine starts accepting individual social data with re-spect to keyword that it analyses and interprets, and then classifies it as positive, negative or neutral with respect to the query term. Keyword: Machine Learning; Natural Language Processing; Supervised Learning; Text Analysis URL: http://research.ijcaonline.org/volume99/number13/pxc3898274.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.678.7797 BASE Hide details 1077 Deep Learning with Constraints for Answer-Agnostic Question Generation in Legal Text Understanding Lamba, Deepti. - August BASE Show details 1078 Convolutional neural network language models Boleda, Gemma; Pham, Nghia The; Kruszewski, German. - : ACL (Association for Computational Linguistics) BASE Show details 1079 A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion Liparas, Dimitris; Symeonidis, Spyridon; Vrochidis, Stefanos. - : Springer BASE Show details 1080 The LAMBADA dataset: word prediction requiring a broad discourse context Kruszewski, German; Fernandez, Raquel; Baroni, Marco. - : ACL (Association for Computational Linguistics) BASE Show details Page: 1...50 51 52 53 54

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