Analysis and Assessment of Cross-Language Question Answering Systems

Analysis and Assessment of Cross-Language Question Answering Systems

Juncal Gutiérrez-Artacho (University of Granada, Spain) and María-Dolores Olvera-Lobo (CSIC, Unidad Asociada Grupo SCImago and University of Granada, Spain)
DOI: 10.4018/978-1-5225-7659-4.ch018

Abstract

Within the sphere of the web, the overload of information is more notable than in other contexts. Question answering systems (QAS) are presented as an alternative to the traditional information retrieval (IR) systems seeking to offer precise and understandable answers to factual questions instead of showing the user a list of documents related to a given search. Given that the QAS is presented as a substantial advance in the improvement of IR, it becomes necessary to determine its effectiveness for the final user. With this aim, seven studies were undertaken to evaluate: 1) in the first two, the linguistic resources and tools used in these systems for multilingual retrieval (Research 1, Research 2), and 2) the performance and quality of the answers of the main monolingual and multilingual QA of general domain and specialized domain in the web in response to different types of questions and subjects, so that different evaluation means can be applied (Research 3, Research 4, Research 5, Research 6, Research 7).
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Background

In the field of CLIR tools are being created that can greatly assist specialists in their work; as well as helping other users find a wide variety of information. These tools are evolving but several years of study and research are still needed to improve implementations. One of the main difficulties facing these tools is the task of translating queries made by users and the documentary sources found in response (Diekema, 2003). Given the current expansion in research, development, and the creation of CLIR systems, it was considered worthwhile analysing and evaluating the resources used by one type of these systems: multi-lingual QAS.

Frequently, a keyword query entered into a web search tool (search engine or meta-search engine) to satisfy a user’s information need, provides too many result pages – many of which are useless or irrelevant to the user. In effect, modern IR systems allow us to locate documents that might have the associated information, but the majority of them leave it to the user to extract the useful information from an ordered list (Dwivedi & Singh, 2013). In contrast to the IR scenario, a QAS processes questions formulated into Natural Language instead of keyword based queries, and retrieves answers instead of documents (Peñas et al., 2012). Therefore, the usefulness of these types of systems for quickly and effectively finding specialized information has been widely recognized (Diekema et al., 2004).

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