BibSonomyburst/user/diego_ma/machine_learningBibSonomy RSS feed for /user/diego_ma/machine_learning2012-02-17T00:20:04+01:00http://www.bibsonomy.org/bibtex/261e9c9275679c8c5021603eb6920d033/diego_madiego_ma2009-11-12T21:36:09+01:00classification machine_learning feature_selection <span class="authorEditorList"><a href="/author/Yang">Yiming Yang</a>, and <a href="/author/Pedersen">Jan O. Pedersen</a> </span><em>Proceedings of ICML-97, 14th International Conference on Machine Learning, </em><em>page 412--420. </em><em>Nashville, US, </em><em>Morgan Kaufmann Publishers, San Francisco, US, </em>(<em>1997</em>)Thu Nov 12 21:36:09 CET 2009Nashville, USProceedings of {ICML}-97, 14th International Conference on Machine Learning412--420A comparative study on feature selection in text categorization1997classification machine_learning feature_selection http://www.bibsonomy.org/bibtex/2a36b72928b449ac746792a05d0ec2d7c/diego_madiego_ma2009-11-11T22:33:15+01:00question_answering machine_learning <span class="authorEditorList"><a href="/author/Shen">Dan Shen</a>, <a href="/author/Kruijff">Geert-Jan M. Kruijff</a>, and <a href="/author/Klakow">Dietrich Klakow</a> </span><em>Natural Language Processing ? IJCNLP 2005: Second International Joint Conference, Jeju Island, Korea, October 11-13, 2005. Proceedings., </em><em>Springer-Verlag, </em>(<em>2005</em>)Wed Nov 11 22:33:15 CET 2009Natural Language Processing ? IJCNLP 2005: Second International Joint Conference, Jeju Island, Korea, October 11-13, 2005. Proceedings.Exploring Syntactic Relation Patterns for Question Answering2005question_answering machine_learning In this paper, we explore the syntactic relation patterns for open domain factoid question answering. We propose a pattern extraction method to extract the various relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from syntactic trees. We further propose a QA-specific tree kernel to partially match the syntactic relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the syntactic relation patterns help to improve the performance by 6.91 MRR based on the common features.http://www.bibsonomy.org/bibtex/2b385d2d62a1cec0bcaa6f01019112f65/diego_madiego_ma2009-11-11T22:33:09+01:00question_answering machine_learning dependencies DG <span class="authorEditorList"><a href="/author/Shen">Dan Shen</a>, and <a href="/author/Klakow">Dietrich Klakow</a> </span><em>Proceedings COLING/ACL 2006, </em><em>page 889-896. </em><em>Sydney, </em>(<em>2006</em>)Wed Nov 11 22:33:09 CET 2009SydneyProceedings COLING/ACL 2006889-896Exploring Correlation of Dependency Relation Paths for Answer Extraction2006question_answering machine_learning dependencies DG In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training. Experimental results show that our method significantly outperforms state-ofthe-art syntactic relation-based methods by up to 20% in MRR.http://www.bibsonomy.org/bibtex/25bd3953af92ba73c62bf74126011d01b/diego_madiego_ma2009-09-15T17:44:45+02:00question_classification machine_learning semantic_information <span class="authorEditorList"><a href="/author/Li">Xin Li</a>, and <a href="/author/Roth">Dan Roth</a> </span><em>Journal of Natural Language Engineering</em> <em>12(3):229-249</em> (<em>2006</em>)Tue Sep 15 17:44:45 CEST 2009Journal of Natural Language Engineering3229-249Learning Question Classifiers: The Role of Semantic Information122006question_classification machine_learning semantic_information http://www.bibsonomy.org/bibtex/28516d94c1f7aa1e391ddd3ace4caa23b/diego_madiego_ma2009-04-20T05:42:23+02:00search machine_learning text_categorisation inf_retr web_search clustering <span class="authorEditorList"><a href="/author/Manning">Christopher D. Manning</a>, <a href="/author/Raghavan">Prabhakar Raghavan</a>, and <a href="/author/Schütze">Hinrich Schütze</a> </span><em>Cambridge University Press, </em><em>New York, </em>(<em>2008</em>)Mon Apr 20 05:42:23 CEST 2009New YorkIntroduction to Information Retrieval2008search machine_learning text_categorisation inf_retr web_search clustering TOC 1 - Boolean Retrieval 2 - The term vocabulary and posting lists 3 - Dictionaries and tolerant retrieval 4 - Index construction 5 - Index compression 6 - Scoring, term weighting and the vector space model 7 - Computing scores in a complete search system 8 - Evaluation in information retrieval 9 - Relevance feedback and query expansion 10 - XML retrieval 11 - Probabilistic information retrieval 12 - Language models for information retrieval 13 - Text classification and Naive Bayes 14 - Vector space classification 15 - Support vector machines and machine learning on documents 16 - Flat clustering 17 - Hierarchical clustering 18 - Matrix decompositions and latent semantic indexing 19 - Web search basics 20 - Web crawling and indexes 21 - Link analysishttp://www.bibsonomy.org/bibtex/2722a822b6752526ee0e05c369f3cd6d4/diego_madiego_ma2009-04-20T05:34:59+02:00search machine_learning similarity text_mining nlp inf_retr link_analysis clustering <span class="authorEditorList"><a href="/author/Chakrabarti">Soumen Chakrabarti</a> </span><em>Morgan Kaufmann, </em><em>Amsterdam, </em>(<em>2003</em>)Mon Apr 20 05:34:59 CEST 2009AmsterdamMining the Web: Discovering Knowledge from Hypertext Data2003search machine_learning similarity text_mining nlp inf_retr link_analysis clustering TOC: 1 - Introduction 2 - Crawling the Web 3 - Web search and information retrieval 4 - Similarity and clustering 5 - Supervised learning 6 - Semisupervised learning 7 - Social network analysis 8 - Resource discovery 9 - The future of web mininghttp://www.bibsonomy.org/bibtex/23cb7b8d122f144ccf7957fac478a3ed2/diego_madiego_ma2008-03-04T07:47:30+01:00questions machine_learning <span class="authorEditorList"><a href="/author/Pizzato">Luiz A.S. Pizzato</a> </span><em>Proc. ALTW 2004, </em><em>page 25-31. </em><em>Sydney, Australia, </em><em>Macquarie University, </em><em>ASSTA, </em>(<em>2004</em>)Tue Mar 04 07:47:30 CET 2008Sydney, AustraliaProc. ALTW 200425-31Using a Trie-based Structure for Question Analysis2004questions machine_learning This paper presents an approach for question analysis that defines the question subject and its required answer type by building a trie-based structure from a set of question patterns. The question analysis consists of comparing the question tokens with the path of nodes in the trie. A look-ahead process solve the mismatches of unknown words by assigning a entity-type or semantically linking them with other question words. The developed approach is evaluated using different datasets showing that its performance is comparable with state-of-the-art systems.http://www.bibsonomy.org/bibtex/238e2b0e94fe8709dae88304f30873865/diego_madiego_ma2008-01-29T08:06:06+01:00machine_learning <span class="authorEditorList"><a href="/author/van Zaanen">Menno van Zaanen</a> </span><em>University of Leeds, </em><em>Leeds, UK, </em>(<em>2002</em>)Tue Jan 29 08:06:06 CET 2008Leeds, UKBootstrapping Structure into Language: {A}lignment-{B}ased {L}earning2002machine_learning http://www.bibsonomy.org/bibtex/249b95e3eb33c35b0e237cc9fcb3962ba/diego_madiego_ma2008-01-29T08:01:43+01:00question_answering machine_learning molla_publication <span class="authorEditorList"><a href="/author/van Zaanen">Menno van Zaanen</a>, <a href="/author/Pizzato">Luiz Augusto Pizzato</a>, and <a href="/author/Mollá">Diego Mollá</a> </span><em>String Processing and Information Retrieval: 12th International Conference, SPIRE 2005., </em><em>Springer-Verlag, </em><em>Heidelberg, Germany, </em>(<em>2005</em>)Tue Jan 29 08:01:43 CET 2008Heidelberg, GermanyString Processing and Information Retrieval: 12th International Conference, SPIRE 2005.139-150Classifying Sentences Using Induced Structure2005question_answering machine_learning molla_publication In this article we will introduce a new approach (and several implementations) to the task of sentence classification, where pre-defined classes are assigned to sentences. This approach concentrates on structural information that is present in the sentences. This information is extracted using machine learning techniques and the patterns found are used to classify the sentences. The approach fits in between the existing machine learning and hand-crafting of regular expressions approaches, and it combines the best of both. The sequential information present in the sentences is used directly, classifiers can be generated automatically and the output and intermediate representations can be investigated and manually optimised if needed.http://www.bibsonomy.org/bibtex/233a161454c4867f97377379545d4ad3d/diego_madiego_ma2008-01-29T08:01:27+01:00machine_learning question_answering molla_publication <span class="authorEditorList"><a href="/author/van Zaanen">Menno van Zaanen</a>, <a href="/author/Pizzato">Luiz A.S. Pizzato</a>, and <a href="/author/Mollá">Diego Mollá</a> </span><em>Proc. Twelfth Edition of the Symposium on String Processing and Information Retrieval SPIRE2005, </em><em>Buenos Aires, </em>(<em>2005</em>)Tue Jan 29 08:01:27 CET 2008Buenos AiresProc. Twelfth Edition of the Symposium on String Processing and Information Retrieval (SPIRE2005)Classifying Sentences Using Induced Structure2005machine_learning question_answering molla_publication http://www.bibsonomy.org/bibtex/2753c74267baabe14fe6d420b78043998/diego_madiego_ma2007-12-14T02:48:42+01:00named_entities machine_learning <span class="authorEditorList"><a href="/author/Zhou">GuoDong Zhou</a>, and <a href="/author/Su">Jian Su</a> </span><em>Proc. 40th Annual Meeting of the Association for Computational Linguistics ACL 2002, </em>(<em>2002</em>)Fri Dec 14 02:48:42 CET 2007Proc. 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002)Named Entity Recognition using an {HMM}-based Chunk Tagger2002named_entities machine_learning This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.http://www.bibsonomy.org/bibtex/27f0138f56192ab8b6ee9a58bc8ccd636/diego_madiego_ma2007-12-14T02:48:33+01:00machine_learning grammar <span class="authorEditorList"><a href="/author/van Zaanen">Menno van Zaanen</a> </span><em>COLING 2000 - Proceedings of the 18th International Conference on Computational Linguistics, </em><em>page 961--967. </em>(<em>2000</em>)Fri Dec 14 02:48:33 CET 2007COLING 2000 - Proceedings of the 18th International Conference on Computational Linguistics961--967{ABL}: Alignment-Based Learning2000machine_learning grammar http://www.bibsonomy.org/bibtex/2cb4a3198fa15460df762c602a2fc24fa/diego_madiego_ma2007-12-14T02:48:32+01:00machine_learning grammar <span class="authorEditorList"><a href="/author/van Zaanen">Menno van Zaanen</a> </span><em>Proceedings of the Seventeenth International Conference on Machine Learning, </em><em>page 1063--1070. </em><em>Stanford University, </em><em>Morgan Kaufmann Publishers, </em>(<em>July 2000</em>)Fri Dec 14 02:48:32 CET 2007Proceedings of the Seventeenth International Conference on Machine Learning#jul#1063--1070Bootstrapping Syntax and Recursion using Alignment-Based Learning2000machine_learning grammar http://www.bibsonomy.org/bibtex/2931e40b89a930ecb93229555ebf62a8c/diego_madiego_ma2007-12-14T02:45:31+01:00question_answering machine_learning <span class="authorEditorList"><a href="/author/Ravichandran">Deepak Ravichandran</a>, and <a href="/author/Hovy">Eduard Hovy</a> </span><em>Proc. ACL2002, </em>(<em>2002</em>)Fri Dec 14 02:45:31 CET 2007Proc. ACL2002Learning Surface Text Patterns for a Question Answering System2002question_answering machine_learning In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to Altavista. Patterns are then automatically extracted from the returned documents and standardized. We calculate the precision of each pattern, and the average precision for each question type. These patterns are then applied to find answers to new questions. Using the TREC-10 question set, we report results for two cases: answers determined from the TREC-10 corpus and from the web.http://www.bibsonomy.org/bibtex/2df8b6d0b3bcaa21e52b3d7156013d22c/diego_madiego_ma2007-12-14T02:45:27+01:00machine_learning <span class="authorEditorList"><a href="/author/Quinlan">J. Ross Quinlan</a> </span><em>Machine Learning</em> (<em>1990</em>)Fri Dec 14 02:45:27 CET 2007Machine Learning3Learning Logical Definitions from Examples51990machine_learning http://www.bibsonomy.org/bibtex/2f43ae7517a973e4fcf0ca0dab65248a2/diego_madiego_ma2007-12-14T02:45:05+01:00question_answering machine_learning <span class="authorEditorList"><a href="/author/van der Plas">Lonneke van der Plas</a>, and <a href="/author/Bouma">Gosse Bouma</a> </span><em>Proceedings Ontolex 2005, </em><em>page 9 pages. </em>(<em>2005</em>)Fri Dec 14 02:45:05 CET 2007Proceedings Ontolex 20059 pagesAutomatic Acquisition of Lexico-semantic Knowledge for QA2005question_answering machine_learning We present an experiment for finding semantically similar words on the basis of a parsed corpus of Dutch text and show that the acquired information correlates with relations found in Dutch EuroWordNet. Next, we demonstrate how the acquired knowledge can be used to boost the performance of an open-domain question answering system for Dutch. Automatically acquired lexico-semantic information is used to improve the recall of a method for extracting function relations (such as Wim Kok is the prime minister of the Netherlands) from corpora, and to improve the precision of our QA system on general WH-questions and definition questions.http://www.bibsonomy.org/bibtex/2e620a713b0d1f6cb56a4333e8d8c6cf4/diego_madiego_ma2007-12-14T02:45:02+01:00questions machine_learning <span class="authorEditorList"><a href="/author/Pizzato">Luiz A.S. Pizzato</a> </span><em>Proc. ALTW 2004, </em><em>page 25-31. </em><em>Sydney, Australia, </em><em>Macquarie University, </em><em>ASSTA, </em>(<em>2004</em>)Fri Dec 14 02:45:02 CET 2007Sydney, AustraliaProc. ALTW 200425-31Using a Trie-based Structure for Question Analysis2004questions machine_learning This paper presents an approach for question analysis that defines the question subject and its required answer type by building a trie-based structure from a set of question patterns. The question analysis consists of comparing the question tokens with the path of nodes in the trie. A look-ahead process solve the mismatches of unknown words by assigning a entity-type or semantically linking them with other question words. The developed approach is evaluated using different datasets showing that its performance is comparable with state-of-the-art systems.http://www.bibsonomy.org/bibtex/2e5f9422854abecc6ca1b2727985caaf7/diego_madiego_ma2007-12-14T02:44:56+01:00machine_learning <span class="authorEditorList"><a href="/author/Pereira">Fernando Pereira</a>, <a href="/author/Singer">Yoram Singer</a>, and <a href="/author/Tishby">Naftali Tishby</a> </span><em>Natural Language Processing Using Very Large Corpora, </em><em>volume 11 of Text, Speech and Language Technology, </em><em>Kluwer, </em>(<em>1999</em>)Fri Dec 14 02:44:56 CET 2007Natural Language Processing Using Very Large Corpora121-136Text, Speech and Language TechnologyBeyond Word N-Grams111999machine_learning We describe, analyze, and evaluate experimentally a new probabilistic model for word-sequence prediction in natural language based on prediction suffix trees (PSTs). ...http://www.bibsonomy.org/bibtex/2cec6bc800af4e9678663237f88d3f02a/diego_madiego_ma2007-12-14T02:44:55+01:00machine_learning <span class="authorEditorList"><a href="/author/Pearl">J. Pearl</a> </span>(<em>1992</em>)Fri Dec 14 02:44:55 CET 2007Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference1992machine_learning http://www.bibsonomy.org/bibtex/20a07f14d220a8a7646087114c0f49958/diego_madiego_ma2007-12-14T02:44:19+01:00machine_learning <span class="authorEditorList"><a href="/author/Muggleton">S. Muggleton</a>, and <a href="/author/Feng">C. Feng</a> </span><em>Inductive Logic Programming, </em><em>Academic Press, </em>(<em>1992</em>)Fri Dec 14 02:44:19 CET 2007Inductive Logic ProgrammingEfficient Induction of Logic Programs1992machine_learning