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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 BASE Show details 1077 Deep Learning with Constraints for Answer-Agnostic Question Generation in Legal Text Understanding Lamba, Deepti. - August Abstract: Doctor of Philosophy ; Department of Computer Science ; William H. Hsu ; The aim of this dissertation is to develop constraint-based methods that extend and improve on current deep learning neural networks such as transformers and sequence-to-sequence (seq2seq) models, for the problem of question generation based on the analysis of the text of legal agreements, particularly privacy policies. A privacy policy is a legally binding agreement between a customer and service provider. This dissertation focuses on analyzing a privacy policy document to generate questions that capture entities and the relationships between them. Another area of focus is the generation of constraints based on domain knowledge and their application to the deep learning network during the question generation process. A possible use case of this research is development of test corpus for question answering systems in the privacy domain because the shortage of sufficiently large corpora poses a key challenge in the development of question answering and question generation systems. Question generation is the task of generating an interrogative sentence based on some text. Current approaches to question generation use sequence-to-sequence models with additional information like answers, positions of the answers, part-of-speech details, named entity tags among others. The idea behind such approaches is that these models can benefit from additional information about the text (i.e., sentence or paragraph). Recently, transformer-based approaches that offer the benefit of attention mechanism have also been used for generating questions. Transformers have achieved state-of-the-art results in many natural language processing tasks including text classification, machine translation, language understanding, co-reference resolution, and summarization. However, the contribution of transformers towards a task like question generation has not been as significant. This research tries to find ways of improving existing approaches by injecting domain knowledge, modeled as a combination of logical and linguistic constraints, into these deep learning models during the training and validation phases. This work also explores design and implementation of different kind of constraints that can better direct the deep learning model towards the expected output, which in this case refers to syntactically and semantically correct and relevant questions. Another contribution of this research is the creation of custom labels for named entities in the privacy policy domain. Results show that adding some form of domain specific constraints improves the performance of the aforementioned models as compared to the performance of state-of-the-art models on the test bed used in this work. For the given test bed, constrained seq-to-seq approaches perform better than the constrained transformer-based approach. Keyword: Deep Learning; Legal Text; Natural Language Processing; Privacy Policies URL: https://hdl.handle.net/2097/41629 BASE Hide 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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