Author(s):
Saias, José 
; Quaresma, Paulo
Date: 2012
Persistent ID: http://hdl.handle.net/10174/5406
Origin: Repositório Científico da Universidade de Évora
Subject(s): QA; NLP
Document details
DI@UE in CLEF2012: question answering approach to the multiple choice QA4MRE ch...
Description
In the 2012 edition of CLEF, the DI@UE team has signed up for Question Answering for Machine Reading Evaluation (QA4MRE) main task. For each question, our system tries to guess which of the five hypotheses is the more plausible response, taking into account the reading test content and the documents from the background collection on the question topic. For each question, the system applies Named Entity Recognition, Question Classification, Document and Passage Retrieval. The criteria used in the first run is to choose the answer with the smallest distance between question and answer key elements. The system applies a specific treatment for certain factual questions, with the categories Quantity, When, Where, What, and Who, whose responses are usually short and likely to be detected in the text. For the second run, the system tries to solve each question according to its category. Textual patterns used for answer validation and Web answer projection are defined according to the question category. The system answered to all 160 questions, having found 50 right candidate answers.
In the 2012 edition of CLEF, the DI@UE team has signed up for Question Answering for Machine Reading Evaluation (QA4MRE) main task. For each question, our system tries to guess which of the five hypotheses is the more plausible response, taking into account the reading test content and the documents from the background collection on the question topic. For each question, the system applies Named Entity Recognition, Question Classification, Document and Passage Retrieval. The criteria used in the first run is to choose the answer with the smallest distance between question and answer key elements. The system applies a specific treatment for certain factual questions, with the categories Quantity, When, Where, What, and Who, whose responses are usually short and likely to be detected in the text. For the second run, the system tries to solve each question according to its category. Textual patterns used for answer validation and Web answer projection are defined according to the question category. The system answered to all 160 questions, having found 50 right candidate answers.
Document Type
Article
Language English
Editor(s) Forner, Pamela; Karlgren, Jussi; Womser-Hacker, Christa
Language English
Editor(s) Forner, Pamela; Karlgren, Jussi; Womser-Hacker, Christa
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