In this paper, we propose a keyword-based passage retrieval algorithm for information extraction, trained by distant supervision. Our goal is to be able to extract attributes of people and organizations more quickly and accurately by first ranking all the potentially relevant passages according to their likelihood of containing the answer and then performing a traditional deeper, slower analysi...

Answer passage retrieval is an increasingly important information retrieval task as queries become more precise and mobile and audio interfaces more prevalent. In this task, the goal is to retrieve a contiguous series of sentences (a passage) that concisely addresses the information need expressed in the query. Recent work with deep learning has shown the efficacy of distributed text representa...

Traditional statistical retrieval models often treat each document as a whole. In many cases, however, a document is relevant to a query only because a small part of it contain the targeted information. In this work, we propose a neural passage model (NPM) that uses passagelevel information to improve the performance of ad-hoc retrieval. Instead of using a single window to extract passages, our...

This paper reports our efforts on developing a language modeling approach to passage question answering. In particular, we address the following two problems: (i) generalized language modeling for question classification; (ii) constrained language modeling for passage retrieval.

This year, like in 2006, we use a collection of about 160000 full-text articles. The proposed task is a passage retrieval task. Several measures are applied on the returned data: passage mean average precision, document mean average precision, aspect mean average precision. This year, our efforts concentrated on combining knowledge-driven methods on top of a standard vectorspace retrieval appro...

In this article, we present a novel principled approach to passage-based (document) retrieval using fuzzy set theory. The formulates passage score combination according general relevance decision principles. By operationalizing these principles aggregation operators of theory, our justifies the common heuristics taking maximum constituent as overall document score. Experiments show that is only...

This article describes the participation of the joint Elhuyar-IXA group in the ResPubliQA exercise at QA&CLEF 2010. In particular, we participated in the English–English monolingual task and in the Basque– English cross-lingual one. Our focus was threefold: (1) to check to what extent information retrieval (IR) can achieve good results in passage retrieval without question analysis and answer v...

Automatic open domain question answering (QA) has been the focus of much recent research, stimulated by the introduction of a QA track in TREC in 1999. Many QA systems have been developed and most follow the same broad pattern of operation: first an information retrieval (IR) system, often passage-based, is used to find passages from a large document collection which are likely to contain answe...

Information retrieval is the process of searching for relevant documents that satisfy a user’s information need (usually in the form of queries). Some of its successful applications include library catalogue search, medical record retrieval, and Internet search engines (e.g., Google). As the exponential growth of web pages and online documents continues, there is an increasing need for retrieva...

In this paper, we apply the statistical machine translation based passage retrieval, which was proposed at the last NTCIR-6 CLQA subtask, to the IR4QA Task. The experimental evaluation shows that the method is more effective for the relation and event type questions, which are longer and including relatively mane common keywords, than the definition and biography type questions, which are short...