QA@INEX track 2011: Question expansion and reformulation using the REG summarization system
|QA@INEX track 2011: Question expansion and reformulation using the REG summarization system|
|Author(s)||Vivaldi J., Da Cunha I.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Automatic summarization, INEX, Named entities, Question-answering, REG, Terms, Wikipedia (Extra: Automatic summarization, INEX, Named entities, Question Answering, REG, Terms, Wikipedia, Natural language processing systems, Query languages, Search engines, Websites, Query processing)|
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QA@INEX track 2011: Question expansion and reformulation using the REG summarization system is a 2012 conference paper written in English by Vivaldi J., Da Cunha I. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
In this paper, our strategy and results for the INEX@QA 2011 question-answering task are presented. In this task, a set of 50 documents is provided by the search engine Indri, using some queries. The initial queries are titles associated with tweets. Reformulation of these queries is carried out using terminological and named entities information. To design the queries, the full process is divided into 2 steps: a) both titles and tweets are POS tagged, and b) queries are expanded or reformulated, using: terms and named entities included in the title, terms and named entities found in the tweet related to those ones, and Wikipedia redirected terms and named entities from those ones included in the title. In our work, the automatic summarization system REG is used to summarize the 50 documents obtained with these queries. The algorithm models a document as a graph to obtain weighted sentences. A single document is generated and it is considered the answer of the query. This strategy, combining summarization and question reformulation, obtains good results regarding informativeness and readability.
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