BibSonomy publications for /user/diego_maWed Mar 10 00:39:06 CET 2010Computer834-40Search Engines that Learn from Implicit Feedback402007searchfeedbackweb_search Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted frmo the logs to automatically tailor ranking functions to a particular user group or collection.Wed Mar 10 00:30:29 CET 2010Procesamiento del Lenguaje Natural159-167Describing Biomedical Document Sets in Terms of its Most Distinctive Facts432009biomedicalretrieval In this paper, we propose a method to describe a set of conceptually indexed biomedical documents in terms of its most distinctive facts. These documents are retrieved to support the occurrence of a focus concept, which expresses an information need. The facts used for description are concise information units, represented as triples of the form entity-verb-entity. These are presented as a ranked list, ordered by their relevance with respect to the focus concept, which is determined using a language modeling approach. Experimental results, obtained on three document sets over a collection extracted from MEDLINE, are promising. Keywords: text mining, information retrieval, biomedical applications.Wed Mar 10 00:26:36 CET 2010Procesamiento del Lenguaje Natural131-139Propuesta y evaluaci{\'o}n de un m{\'e}todo extractivo de generaci{\'o}n de res{\'u}menes en el {\'a}mbito biom{\'e}dico basado en conceptos432009summarisationbiomedical Los métodos de generación de resúmenes basados en técnicas extractivas han demostrado ser muy útiles por su adaptabilidad y eficiencia en tiempo de respuesta en cualquier tipo de dominios. En el ámbito biomédico son numerosos los estudios que hablan de la sobrecarga de información y recogen la necesidad de aplicación de técnicas eficientes de recuperación y generación de resúmenes para una correcta aplicación de la medicina basada en la evidencia. En este contexto vamos a presentar una propuesta metodológica de generación automática de resúmenes basada en ontologías y grafos, aplicando técnicas de similitud y la frecuencia de aparición de los conceptos para obtener las frases más relevantes. Se realiza una evaluación de la propuesta frente a otras metodologías con la herramienta ROUGE y se analizan los resultados. Aunque la extensión del conjunto de evaluación no permite extraer conclusiones significativas, los resultados son suficientemente prometedores como para confiar en la efectividad de la propuesta presentada.Mon Mar 01 09:05:00 CET 2010Proceedings ACL 2009Automatically Generating Wikipedia Articles: A Structure-Aware Approach2009summarisationmultidocumentgenerationwikipedia In this paper, we investigate an approach for creating a comprehensive textual overview of a subject composed of information drawn from the Internet. We use the high-level structure of human-authored texts to automatically induce a domain-specific template for the topic structure of a new overview. The algorithmic innovation of our work is a method to learn topic-specific extractors for content selection jointly for the entire template. We augment the standard perceptron algorithm with a global integer linear programming formulation to optimize both local fit of information into each topic and global coherence across the entire overview. The results of our evaluation confirm the benefits of incorporating structural information into the content selection process.Mon Mar 01 08:58:21 CET 2010Computational LinguisticsSeptember3297--328Sentence Fusion for Multidocument News Summarization312005summarisation Sun Feb 07 06:11:07 CET 2010Inf. Retr.5565-587Methods for automatically evaluating answers to complex questions.92006clustering biomedical question_analysis Thu Feb 04 04:12:52 CET 2010Athens, GreeceProceedings of Conference of the European Chapter of the Association for Computational Linguistics(EACL 2009)Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument Satisfaction Model2009summarisation Thu Feb 04 04:07:35 CET 2010Proceedings of the International Conference on User Modelling, Adaptation and Presentation (UMAP 2009)Capturing the User's Reading Context for Tailoring Summaries2009summarisation The web has become a major source of information to learn about a topic. With the continuous growth of information and its high connectivity, it is hard to follow only the links that are relevant and not to get lost in hyperspace. Our aim is to support people who read documents in a highly connected information space, helping them remain on focus. Our contextually-aware in-browser text summarisation tool, IBES, does this by capturing users? current interests and providing users with contextualised summaries of linked documents, to help them decide whether the link is worth following.Thu Feb 04 04:04:54 CET 2010Manchester, United KingdomProceedings of the 22nd International Conference on Computational Linguistics689-696Scientific paper summarization using citation summary networks2008jabref:noKeywordAssigned Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach.Thu Feb 04 02:56:27 CET 2010Austin, TexasProceedings of the 2009 Joint Conference on Digital Libraries59-69Whetting the Appetite of Scientists: Producing Summaries Tailored to the Citation Context2009summarisation Wed Feb 03 05:53:04 CET 2010Proceedings of MLDM265--274CorePhrase: keyphrase extraction for document clustering2005clustering Wed Feb 03 05:51:36 CET 2010Journal of Decision Support Systems81--104Improving browsing in digital libraries with keyphrase indexes271999jabref:noKeywordAssigned Wed Feb 03 05:48:17 CET 2010Sydney, AustraliaProceedings of Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21st International Conference on Computational Linguistics491--498Interpreting Semantic Relations in Noun Compounds via Verb Semantics2006jabref:noKeywordAssigned Wed Feb 03 05:45:59 CET 2010Jeju, KoreaProceedings of 2nd International Joint Conference on Natual Language Processing945--956Automatic Interpretation of Compound Nouns using {WordNet} Similarity2005compound_nouns WordNet Wed Feb 03 05:44:20 CET 2010Boston, USAProceedings of the HLT-NAACL 2004: Workshop on Computational Lexical Semantics60--67Models for the Semantic Classification of Noun Phrases2004compound_nouns Wed Feb 03 05:39:40 CET 2010New Orleans, Louisiana, USAProceedings of SIGIR 2001349--357Finding Topic Words for Hierarchical Summarization2001summarisation Wed Feb 03 05:37:58 CET 2010Proceedings of the ACL/EACL 1997 Workshop on Intelligent Scalable Text Summarization10--17Using lexical chains for text summarization1997summarisation Wed Feb 03 05:33:46 CET 2010Proceedings of Document Understanding Conferences6-8A Keyphrase-Based Approach to Summarization:the LAKE System2005summarisation Wed Feb 03 05:18:03 CET 2010Proceedings HIKM 20108 pagesA Study on the Use of Search Engines for Answering Clinical Questions2010search 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 Feb 03 05:17:16 CET 2010Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain2002biomedical nat_lang