40 research outputs found
Query-based extracting: how to support the answer?
Human-made query-based summaries commonly contain information not explicitly asked for. They answer the user query, but also provide supporting information. In order to find this information in the source text, a graph is used to model the strength and type of relations between sentences of the query and document cluster, based on various features. The resulting extracts rank second in overall readability in the DUC 2006 evaluation. Employment of better question answering methods is the key to improve also content-based evaluation results
Query-Based Summarization using Rhetorical Structure Theory
Research on Question Answering is focused mainly on classifying the question type and finding
the answer. Presenting the answer in a way that suits the userâs needs has received little
attention. This paper shows how existing question answering systemsâwhich aim at finding
precise answers to questionsâcan be improved by exploiting summarization techniques to extract
more than just the answer from the document in which the answer resides. This is done
using a graph search algorithm which searches for relevant sentences in the discourse structure,
which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a
graph representation of a text document. The output is an extensive answer, which not only
answers the question, but also gives the user an opportunity to assess the accuracy of the answer
(is this what I am looking for?), and to find additional information that is related to the question,
and which may satisfy an information need. This has been implemented in a working multimodal
question answering system where it operates with two independently developed question
answering modules
Normalized Alignment of Dependency Trees for Detecting Textual Entailment
In this paper, we investigate the usefulness of normalized alignment of dependency trees for entailment prediction. Overall, our approach yields an accuracy of 60% on the RTE2 test set, which is a significant improvement over the baseline. Results vary substantially across the different subsets, with a peak performance on the summarization data. We conclude that
normalized alignment is useful for detecting textual entailments, but a robust approach will probably need to include additional sources of information
Illustrating answers: an evaluation of automatically retrieved illustrations of answers to medical questions
In this paper we discuss and evaluate a method for automatic text illustration, applied to answers to medical questions. Our method for selecting illustrations is based on the idea that similarities between the answers and picture-related text (the pictureâs caption or the section/paragraph that includes the picture) can be used as evidence that the picture would be appropriate to illustrate the answer.In a user study, participants rated answer presentations consisting of a textual component and a picture. The textual component was a manually written reference answer; the picture was automatically retrieved by measuring the similarity between the text and either the pictureâs caption or its section. The caption-based selection method resulted in more attractive presentations than the section-based method; the caption-based method was also more consistent in selecting informative pictures and showed a greater correlation between user-rated informativeness and the confidence of relevance of the system.When compared to manually selected pictures, we found that automatically selected pictures were rated similarly to decorative pictures, but worse than informative pictures
Kyoto: An Integrated System for Specific Domain WSD
This document describes the preliminary release of the integrated Kyoto system for specific domain WSD. The system uses concept miners (Tybots) to extract domain-related terms and produces a domain-related thesaurus, followed by knowledge-based WSD based on wordnet graphs (UKB). The resulting system can be applied to any language with a lexical knowledge base, and is based on publicly available software and resources. Our participation in Semeval task #17 focused on producing running systems for all languages in the task, and we attained good results in all except Chinese. Due to the pressure of the time-constraints in the competition, the system is still under development, and we expect results to improve in the near future
Propuesta de modelo de negocio de un food truck de venta de desayunos en una universidad privada de Chiclayo, 2016
El presente trabajo tiene como objetivo establecer un modelo de negocio para un food truck de desayunos en una universidad privada de Chiclayo. La metodologĂa aplicada para la investigaciĂłn es cualitativa â exploratoria, se fundamenta en un proceso inductivo (explorar, describir y luego generar perspectivas teĂłricas), es decir va de lo particular a lo general; esta metodologĂa permite obtener informaciĂłn en base a entrevistas realizadas a la comunidad universitaria. La investigaciĂłn busca conocer la aceptaciĂłn del modelo de food truck de venta de desayuno, se basĂł en el modelo Lean Canvas, desarrollado en el libro Running Lean de Ash Maurya, nos da un enfoque de nueve (9) dimensiones para tener en cuenta y poder lograr un modelo de negocio de ĂŠxito. La propuesta de valor obtenida, consiste en vender productos saludables que les ayude a promover la calidad y bienestar de la salud de nuestros clientes, por ello se ofrecerĂĄn desayunos elaborados a base de frutas, cereales andinos y sĂĄndwich preparados al instante, ofrecidos en unos envases biodegradables, cumpliendo con los estĂĄndares de salubridad. Asimismo se tendrĂĄ variedad en los productos a ofrecer, para que el cliente pueda escoger y se brindarĂĄ una atenciĂłn rĂĄpida y personalizada con la finalidad de cumplir con uno de los aspectos que los clientes valoran.Tesi
Query-based extracting: how to support the answer?
Human-made query-based summaries commonly contain information not explicitly asked for. They answer the user query, but also provide supporting information. In order to find this information in the source text, a graph is used to model the strength and type of relations between sentences of the query and document cluster, based on various features. The resulting extracts rank second in overall readability in the DUC 2006 evaluation. Employment of better question answering methods is the key to improve also content-based evaluation results