Yahoo! Answers for Sentence Retrieval in Question Answering
Saeedeh Momtazi and Dietrich Klakow
In: LREC Workshop on Web Logs and Question Answering, 22 May 2010, La Valletta, Malta.
Question answering systems which automatically search for user’s information need are considered as a separate issue from the
community-generated question answering which answers users’ questions by human respondents. Although the two answering systems
have different applications, both of them aim to present a correct answer to the users’ question and consequently they can feed each
other to improve their performance and efficiency. In this paper, we propose a new idea to use the information derived from a community
question answering forum in an automatic question answering system. To this end, two different frameworks, namely the class-based
model and the trained trigger model, have been used in a language model-based sentence retrieval system. Both models try to capture
word relationships from the question-answer sentence pair of a community forum. Using a standard TREC question answering dataset,
we evaluate our proposed models on the subtask of sentence retrieval, while training the models on the Yahoo! Answer corpus. Results
show both methods that trained on Yahoo! Answers logs significantly outperform the unigram model, in which the class-based model
achieved 4.72% relative improvement in mean average precision and the trained triggering model achieved 18.10% relative improvement
in the same evaluation metric. Combination of both proposed models also improved the system mean average precision 19.29%.