BibSonomy publications for /user/diego_ma/question_answeringWed Feb 03 05:18:03 CET 2010Proceedings HIKM 2010A Study on the Use of Search Engines for Answering Clinical Questions2010molla_medicalnlp search inf-retr question_answering biomedical molla_publication This paper describes an evaluation of the answerability of a set of clinical questions posed by physicians. The clinical questions belong to two categories of the five-leaf high-level hierarchical Evidence Taxonomy created by Ely and his colleagues: Intervention and Non Intervention. The questions are passed to two search engines (PubMed, Google), two question-answering systems (MedQA, Answers.com's BrainBoost), and a dictionary (OneLook) for locating the answers to the question corpus. The output of the systems is judged by a human and scored according to the Mean Reciprocal Rank (MRR). The results show the need for question modification and analyse the impact of specific types of modifications. The results also show that No Intervention questions are easier to answer than Intervention questions. Further, generic search engines like Google obtain higher MRR than specialised systems and even higher than a version of Google based on specialised literature (PubMed) only. In addition, an analysis of the location of the answer in the returned documents is provided.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.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.Wed Nov 11 21:29:27 CET 2009Proc. ACL03 workshop on Multilingual Summarization and Question AnsweringStatistical {QA} - Classifier vs. Re-ranker: What's the Difference?2003question_answering statistical_nlp maxent In this paper, we show that we can obtain a good baseline performance for Question Answering (QA) by using only 4 simple features. Using these features, we contrast two approaches used for a Maximum Entropy based QA system. We view the QA problem as a classification problem and as a reranking problem. Our results indicate that the QA system viewed as a reranker clearly outperforms the QA system used as a classifier. Both systems are trained using the same data.Wed Oct 21 23:50:43 CEST 2009Proc BioNLP 2009171-178Evaluation of the Clinical Question Answering Presentation.2009biomedical question_answering evaluation Question answering is different from information retrieval in that it attempts to answer questions by providing summaries from numerous retrieved documents rather than by simply providing a list of documents that requires users to do additional work. However, the quality of answers that question answering provides has not been investigated extensively, and the practical approach to presenting question answers still needs more study. In addition to factoid answering using phrases or entities, most question answering systems use a sentence- based approach for generating answers. However, many sentences are often only meaningful or understandable in their context, and a passage-based presentation can often provide richer, more coherent context. However, passage-based presentations may introduce additional noise that places greater burden on users. In this study, we performed a quantitative evaluation on the two kinds of presentation produced by our online clinical question answering system, AskHERMES (http://www.AskHERMES.org). The overall finding is that, although irrelevant context can hurt the quality of an answer, the passage-based approach is generally more effective in that it provides richer context and matching across sentences.Wed Oct 21 23:09:17 CEST 2009Information Processing \& Management1619-1642Satisfying Information Needs with Multi-document Summaries432007summarisation question_answering Generating summaries that meet the information needs of a user relies on (1) several forms of question decomposition; (2) different summarization approaches; and (3) textual inference for combining the summarization strategies. This novel framework for summarization has the advantage of producing highly responsive summaries, as indicated by the evaluation results.Thu Sep 17 23:46:31 CEST 2009Los Angeles, CAProceedings Western Joint IRE-AIEE-ACM Computing Conference219--224BASEBALL: An automatic question answerer191961question_answering Tue Sep 15 17:22:44 CEST 2009Traitement Automatique des Langues (TAL)315-39Linguistic Knowledge and Question Answering462005question_answering DG Fri Aug 28 22:25:10 CEST 2009AMIA Annu Symp Proc.458-462Semantic Clustering of Answers to Clinical Questions2007question_answering biomedical Access to clinical evidence is a critical component of the practice of evidence-based medicine. Advanced retrieval systems can supplement precompiled secondary sources to assist physicians in making sound clinical decisions. This study explores one particular issue related to the design of such retrieval systems: the effective organization of search results to facilitate rapid understanding and synthesis of potentially relevant information. We hypothesize that grouping retrieved MEDLINE� citations into semantically-coherent clusters, based on automatically-extracted interventions from the abstract text, represents an effective strategy for presenting results, compared to a traditional ranked list. Experiments with our implemented system appear to support this claim.Fri Aug 28 21:57:28 CEST 2009AMIA Annu Symp Proc.96-100Automatically Extracting Information Needs from Ad Hoc Clinical Questions2008biomedical question_answering question_analysis Automatically extracting information needs from ad hoc clinical questions is an important step towards medical question answering. In this work, we first explored supervised machine-learning approaches to automatically classify an ad hoc clinical question into general topics. We then explored both unsupervised and supervised methods for automatically extracting keywords from an ad hoc clinical question. Our methods were evaluated on the 4,654 clinical questions maintained by the National Library of Medicine. Our best systems or methods showed F-score of 76% for the task of question-general topic classification and of 58% for extracting keywords from ad hoc clinical questions.Fri Aug 28 21:47:37 CEST 2009Proceedings ACLAnswer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering.2006clinical clustering question_answering summarisation Fri Aug 28 21:46:37 CEST 2009AMIA Annu Symp Proc.359�363Evaluation of {PICO} as a Knowledge Representation for Clinical Questions2006PICO biomedical question_answering The paradigm of evidence-based medicine (EBM) recommends that physicians formulate clinical questions in terms of the problem/population, intervention, comparison, and outcome. Together, these elements comprise a PICO frame. Although this framework was developed to facilitate the formulation of clinical queries, the ability of PICO structures to represent physicians� information needs has not been empirically investigated. This paper evaluates the adequacy and suitability of PICO frames as a knowledge representation by analyzing 59 real-world primary-care clinical questions. We discovered that only two questions in our corpus contain all four PICO elements, and that 37% of questions contain both intervention and outcome. Our study reveals prevalent structural patterns for the four types of clinical questions: therapy, diagnosis, prognosis, and etiology. We found that the PICO framework is primarily centered on therapy questions, and is less suitable for representing other types of clinical information needs. Challenges in mapping natural language questions into PICO structures are also discussed. Although we point out limitations of the PICO framework, our work as a whole reaffirms its value as a tool to assist physicians practicing EBM.Mon Aug 17 09:24:28 CEST 2009Proc. TAC 2008Overview of the TAC 2008 Opinion Question Answering and Summarization Tasks2008summarisation question_answering Fri Aug 14 09:16:09 CEST 2009Proc. IJCAI'05 Workshop on Knowledge and Reasoning for Answering QuestionsBeing Erlang Shen: Identifying Answerable Questions2005question_classification question_answering biomedical answerability Research has shown that answers do not exist in biomedical corpora for many questions posed by physicians. We have therefore developed a question filtering component that determines whether or not a posed question is answerable. Using 200 clinical questions that have been annotated by physicians to be answerable or unanswerable, we have explored the use of supervised machine-learning algorithms to automatically classify questions into one of these two categories. We also have incorporated semantic features from a large biomedical knowledge terminology. Our results show that incorporating semantic features in general enhances the performance of question classification and the best system is a probabilistic indexing system that achieves an 80.5% accuracy. Our analysis also shows that stop words may play an important role for separating Answerable from Unanswerable.Fri Aug 14 09:16:01 CEST 2009Proc. AAAI'05 Workshop on Question Answering in Restricted DomainsClassifying Medical Questions based on an Evidence Taxonomy2005question_classification question_answering biomedical We present supervised machine-learning approaches to automatically classify medical questions based on a hierarchical evidence taxonomy created by physicians. We show that SVMs is the best classifier for this task and that a ladder approach, which incorporates the knowledge representation of the hierarchical evidence taxonomy, leads to the highest performance. We have explored the use of features from a large, robust biomedical knowledge resource, namely, the Unified Medical Language System (UMLS), and we have found that performance is generally enhanced by including these features in addition to bag-of-words.Wed Jul 22 09:58:28 CEST 2009MICAI 2006: Advances in Artificial Intelligence996-1006Lecture Notes in Computer ScienceApplying NLP Techniques and Biomedical Resources to Medical Questions in QA Performance.42932006questions question_answering biomedical Nowadays, there is an increasing interest in research on QA over restricted domains. Concretely, in this paper we will show the process of question analysis in a medical QA system. This system is able to obtain answers to different natural language questions according to a question taxonomy. In this system we combine the use of NLP techniques and biomedical resources. The main NLP technique is the use of logic forms and the pattern matching technique in this question analysis performance.Wed May 27 07:50:20 CEST 2009Proceedings of the 28th European Conference on Information Retrieval (ECIR 2006)515-518Web-based Multiple Choice Question Answering for English and Arabic Questions2006multiple_choice www question_answering Answering multiple-choice questions, where a set of possible answers is provided together with the question, constitutes a simplified but nevertheless challenging area in question answering research. This paper introduces and evaluates two novel techniques for answer selection. It furthermore analyses in how far performance figures obtained using the English language Web as data source can be transferred to less dominant languages on the Web, such as Arabic. Result evaluation is based on questions from both the English and the Arabic versions of the TV show "Who wants to be a Millionaire?" as well as on the TREC-2002 QA data.Tue Mar 31 10:24:13 CEST 2009In Proceedings of the Fifteenth Text REtrieval Conference (TREC)The Ephyra QA System at TREC 2006.2006question_answering ephyra The Ephyra QA system has been developed as a flexibleopen-domain QA framework. This framework allows usto combine several techniques for question analysis andanswer extraction and to incorporate multiple knowledgebases to best fit the requirements of the TREC QAtrack, in which we participated this year for the firsttime. The techniques used include pattern learning andmatching, answer type analysis and redundancy eliminationthrough filters. In this paper, we give an overviewof the Ephyra system as used within TREC 2006 andanalyze the system's performance in the QA track.Tue Mar 31 10:22:24 CEST 2009Working Notes of CLEF 2008Multi-lingual Question Answering using OpenEphyra2008question_answering Fri Mar 27 09:05:20 CET 2009Berlin / HeidelbergAdvances in Multilingual and Multimodal Information Retrieval249-256Lecture Notes in Computer ScienceOverview of {QAST 2007}5152/20082007speech question_answering This paper describes QAST, a pilot track of CLEF 2007 aimed at evaluating the task of Question Answering in Speech Transcripts. The paper summarizes the evaluation framework, the systems that participated and the results achieved. These results have shown that question answering technology can be useful to deal with spontaneous speech transcripts, so for manually transcribed speech as for automatically recognized speech. The loss in accuracy from dealing with manual transcripts to dealing with automatic ones implies that there is room for future reseach in this area.