Cross-Language Question Answering Task
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A Cross-Language Question Answering Task is a cross-lingual text mining task for finding the answer to a question in multilingual document collection.
- AKA: CLQA.
- Counter-Example(s):
- See: Ontology, Text Mining, Machine Translation Task, Parallel Corpus, Latent Semantic Analysis.
References
2011
- (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Cross-Language Question Answering.” In: (Sammut & Webb, 2011) p.299 - 305
- Cross-Language Question Answering(CLQA): Question answering is the task of automatically finding the answer to a specific question in a document collection. While in practice this vague description can be instantiated in many different ways, the sense in which the term is mostly understood is strongly influenced by the task specification formulated by the National Institute of Science and Technology (NIST) of the United States for its TREC evaluation conferences (see above). In this sense, the task consists in identifying a text snippet, i.e., a substring, of a predefined maximal length (e.g., 50 characters, or 200 characters) within a document in the collection containing the answer. Different classes of questions are considered:
- Questions around facts and events.
- Questions requiring the definition of people, things and organizations.
- Questions requiring as answer lists of people, objects or data.
- Cross-Language Question Answering(CLQA): Question answering is the task of automatically finding the answer to a specific question in a document collection. While in practice this vague description can be instantiated in many different ways, the sense in which the term is mostly understood is strongly influenced by the task specification formulated by the National Institute of Science and Technology (NIST) of the United States for its TREC evaluation conferences (see above). In this sense, the task consists in identifying a text snippet, i.e., a substring, of a predefined maximal length (e.g., 50 characters, or 200 characters) within a document in the collection containing the answer. Different classes of questions are considered:
- Most proposals for solving the QA problem proceed by first identifying promising documents (or document segments) by using information retrieval techniques treating the question as a query, and then performing some finer-grained analysis to converge to a sufficiently short snippet. Questions are classified in a hierarchy of possible “question types.” Also, documents are preliminarily indexed to identify elements (e.g., person names) that are potential answers to questions of relevant types (e.g., “Who” questions).
- Cross-language question answering (CLQA) is the extension of this task to the case where the collection contains documents in a language different than the language of the question. In this task a CLIR step replaces the monolingual IR step to shortlist promising documents. The classification of the question is generally done in the source language.