Searching Health Information in Question-Answering Systems

Searching Health Information in Question-Answering Systems

María-Dolores Olvera-Lobo (CSIC, Spain & Universidad de Granada, Spain) and Juncal Gutiérrez-Artacho (Universidad de Granada, Spain & Universidad Pablo de Olavide, Spain)
DOI: 10.4018/978-1-4666-3986-7.ch025
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Abstract

Question-Answering Systems (QA Systems) can be viewed as a new alternative to the more familiar Information Retrieval Systems. These systems try to offer detailed, understandable answers to factual questions, in order to retrieve a collection of documents related to a particular search (Jackson & Schilder, 2005). The authors carry out a study to evaluate the quality and efficiency of open- and restricted-domain QA systems as sources for physicians and users in general through one monolingual evaluation and another multilingual. Their objective led them to use definition-type questions in order to evaluate QA systems and determine if they are useful to retrieve medical information. In addition, they analyze and evaluate the results obtained, and identify the source or sources used by the systems and their procedure (Olvera-Lobo & Gutiérrez-Artacho, 2010, 2011).
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Introduction

The advent of the Web and its subsequent expansion have provided the general public with access to enormous volumes of information, offering unquestionable benefits. Nevertheless, this has also brought disadvantages such as overloads of information—which in this environment is even more acute—or the fact that much of the information is incorrect, incomplete, or inaccurate, whether intentionally so or not. Consequently, it becomes indispensable to develop tools and procedures that enable the user to acquire reliable information that is relevant for a particular consultation. This is the challenge that faces Information Retrieval (IR)

Information Retrieval is a discipline focused in the problems of information items’ selection from a storage system in order to facilitate retrieval for the users’ needs (Salton, 1970). Traditionally, IR is understood as a fully automatic process that responds to a user query by examining a collection of documents and returning a sorted document list that should be relevant to the user requirements as expressed in the query (Baeza-Yates & Ribeiro-Nieto, 1999). Simply stated, it could be said that retrieval implies finding certain requested information in a storage system or database of information (Meadow, 1993). An optimal IR system recovers all the relevant documents (implying an exhaustive search, i.e. a high recall) and only the relevant documents (implying perfect accuracy, that is to say, a high precision). This traditional model involves many implied restrictions: a) the assumption that users want full-text documents, rather than answers, and that the query will satisfied with these documents; b) that the process is direct and unidirectional rather than interactive; c) and finally, that the query and document share the same language.

Information overload is felt more strongly on the Web than elsewhere. All too often a query made with a Web search tool (search engine or meta-search engine) results in the retrieval of too many pages—many of which are useless or irrelevant to the user. Question Answering systems (QA systems) are an evolutionary improvement in IR systems. As alternative traditional IR systems, they give correct and understandable answers to factual questions (Pérez-Coutiño et al., 2004)—rather than just offering a list of documents related to the search. The benefit is that users do not have to read whole documents to find the desired information. Therefore, professionals from various areas are beginning to recognize the usefulness of these systems, for quickly and effectively finding specialized information (Crouch et al., 2005).

In recent years, some of the efforts to improve IR in the Web have focused on the design and development of the so-called QA systems. The development of the QA Systems gained strong impetus in the conference on information retrieval TREC (Text REtrieval Conference1)—primarily beginning with TREC-8 (Vorhees, 1999)—which since 1992 has constituted an important international forum to unite and foment research in different areas of information retrieval.

We have carried out a research to evaluate the quality and efficiency of open- and restricted-domain QA systems as sources for physicians and users in general. Our objective led us to use definition-type questions in order to evaluate QA systems and determine if there are useful to retrieve medical information. Also we analyzed and evaluated the results obtained, and identified the source or sources used by the systems and their procedure (Olvera-Lobo & Gutiérrez-Artacho, 2010; Olvera-Lobo & Gutiérrez-Artacho, 2011). So we have carried out two evaluations, one in four monolingual QA systems and the other in a multilingual QA system.

Key Terms in this Chapter

Question Answering Systems: Question Answering systems are an evolutionary improvement in IR systems. As an alternative to traditional IR systems they give correct and understandable answers to factual questions – rather than just offering a list of documents related to the search.

CLIR (Cross-Lingual Information Retrieval): CLIR involves at least two languages in this process. In a multi-lingual environment such as the Web, most IR systems (search engines) are limited to finding documents in the language of the query; or alternatively, include machine translation systems, which are only useful once the documents are located and do not effectively cross the language barrier.

Restricted-Domain QA Systems: This kind of question answering systems deals with questions under a specific domain, and can be seen as an easier task because natural language processing systems can exploit domain-specific knowledge frequently formalized in ontologies. Alternatively, these systems might refer to a situation where only a limited kind of questions is accepted.

Information Retrieval: Information Retrieval is understood as a fully automatic process that responds to a user query by examining a collection of documents and returning a sorted document list that should be relevant to the user requirements as expressed in the query.

Multi-Lingual Question Answering Systems: In multi-lingual QA systems, the language of the question may differ from the language of the retrieved document. These systems are a set of coordinated monolingual systems in which each extracts responses from a collection of separate monolingual documents.

Biomedical Information: Biomedical research is the basic research, applied research, or translational research conducted to aid and support the body of knowledge in the field of medicine. Medical research can be divided into two general categories: the evaluation of new treatments for both safety and efficacy in what are termed clinical trials, and all other research that contributes to the development of new treatments. The latter is termed preclinical research if its goal is specifically to elaborate knowledge for the development of new therapeutic strategies. A new paradigm to biomedical research is being termed translational research, which focuses on iterative feedback loops between the basic and clinical research domains to accelerate knowledge translation from the bedside to the bench, and back again.

Evaluation Measures: Many different measures for evaluating the performance of information retrieval systems have been proposed. The evaluation of QA systemS has been carried out by the traditional evaluation measures based on the relevance (precision and MAP), and also, by other specific measures like MRR, TRR, and FHS.

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