This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Most question answering systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. We use this redundancy to simplify the query rewrites that we need to use, and to support answer mining from returned snippets. Our system performs quite well given the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching passages. Simple passage ranking and n-gram extraction techniques work well in our system making it efficient to use with many backend retrieval engines.

For the short, factoid questions in TREC, the query terms we get from the original questions are either too brief or often do not contain most relevant information in the corpus. It will be very difficult to find the answer (especially exact answer) in a large text document collection because of the gap between the query space and the document space. In order to bridge this gap, there is a need to expand the original queries to include the terms in the document space. In this research, we investigate the integration of both the Web and WordNet in performing local context and lexical correlations to bridge the gap. In order to minimize the noise introduced by the external resources, we explore detailed question classes, fine-grained named entities, and successive constraint relaxation.

Abstract. In this paper, we present Microsearch, a search system suitable for small devices used in ubiquitous computing environments. Akin to a desktop search engine, Microsearch indexes the information inside a small device, and accurately resolves user queries. Given the very limited hardware resources, conventional search engine designs and algorithms cannot be used. We adopt information retrieval techniques for query resolution, and propose a space efficient algorithm to perform top-k query on limited hardware resources. Finally, we present a theoretical model of Microsearch to better understand the tradeoffs in system design parameters. By implementing Microsearch on actual hardware for evaluation, we demonstrate the feasibility of scaling down information retrieval systems onto very small devices. 1

This paper describes the retrieval experiments for the main task and list task of the TREC-2002 question-answering track. The question answering system described automatically finds answers to questions in a large document collection. The system uses a two-stage retrieval approach to answer finding based on matching of named entities, linguistic patterns, keywords, and the use of a new inference module. In answering a question, the system carries out a detailed query analysis that produces a logical query representation, an indication of the question focus, and answer clue words.

This work concerns a question answering tool that uses multiple Web search engines and Web question answering systems to retrieve snippets of text that may contain an exact answer for a natural language question. The method described here treats each Web information retrieval system in a unique manner in order to extract the best results they can provide. The results obtained suggest that our method is comparable with some of today’s state-of-the-art systems. 1

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.

This paper illustrates ongoing research and issues faced when dealing with real-time questions in the domain of Reusable Launch Vehicles (aerospace engineering). The question -answering system described in this paper is used in a collaborative learning environment with real users and live questions. The paper describes an analysis of these more complex questions as well as research to include the user in the question-answering process by implementing a question negotiation module based on the traditional reference interview.

In this article, we present Microsearch, a search system suitable for embedded devices used in ubiquitous computing environments. Akin to a desktop search engine, Microsearch indexes the information inside a small device, and accurately resolves a user’s queries. Given the limited hardware, conventional search engine design and algorithms cannot be used. We adopt Information Retrieval (IR) techniques for query resolution, and proposed a new space-efficient top-k query resolution algorithm. A theoretical model of Microsearch is given to better understand the trade-offs in design parameters. Evaluation is done via actual implementation on off-the-shelf hardware.

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