Abstract
The article presents the experiments carried out as part of the participation in the QA track of INEX 2011. We have submitted two runs. The INEX QA task has two main sub tasks, Focused IR and Automatic Summarization. In the Focused IR system, we first preprocess the Wikipedia documents and then index them using Nutch. Stop words are removed from each query tweet and all the remaining tweet words are stemmed using Porter stemmer. The stemmed tweet words form the query for retrieving the most relevant document using the index. The automatic summarization system takes as input the query tweet along with the tweet’s text and the title from the most relevant text document. Most relevant sentences are retrieved from the associated document based on the TF-IDF of the matching query tweet, tweet’s text and title words. Each retrieved sentence is assigned a ranking score in the Automatic Summarization system. The answer passage includes the top ranked retrieved sentences with a limit of 500 words. The two unique runs differ in the way in which the relevant sentences are retrieved from the associated document. Our first run got the highest score of 432.2 in Relaxed metric of Readability evaluation among all the participants.
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Bhaskar, P., Banerjee, S., Neogi, S., Bandyopadhyay, S. (2012). A Hybrid QA System with Focused IR and Automatic Summarization for INEX 2011. In: Geva, S., Kamps, J., Schenkel, R. (eds) Focused Retrieval of Content and Structure. INEX 2011. Lecture Notes in Computer Science, vol 7424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35734-3_18
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DOI: https://doi.org/10.1007/978-3-642-35734-3_18
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