Meanings are more than just words: A Cross-Domain Question Answering Tool based on Unsupervised Semantic Feature Learning
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2017 by IJCTT Journal | ||
| Volume-53 Number-2 |
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| Year of Publication : 2017 | ||
| Authors : Nripa Chetry, Debanjan Choudhary, Arindam Chatterjee, Dr. Gopichand Agnihotram | ||
| DOI : 10.14445/22312803/IJCTT-V53P113 |
Nripa Chetry, Debanjan Choudhary, Arindam Chatterjee, Dr. Gopichand Agnihotram "Meanings are more than just words: A Cross-Domain Question Answering Tool based on Unsupervised Semantic Feature Learning". International Journal of Computer Trends and Technology (IJCTT) V53(2):64-67, November 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
The state-of-the-art Question Answering (QA) systems, either focus on the similarity in occur-rences of words, or similarity of passages. In this paper, we present a novel QA system, which strikes a harmony between word based similarity and context similarity based on short texts, rather than entire passages. Our system can be vaguely categorized as a cross domain FAQ based QA system. It tracks word based similarity through Latent Semantic Indexing (LSI) and then re-ranks the LSI results using an eXtreme Gradient Boosting(xgboost) classifier model. Sev-eral features trained from word embedding vectors, learned from the domain corpus are fed into the xgboost classifier. These features capture the semantic understanding of the questions or headings/sub-headings in our knowledge base. We have observed from our experiments that us-ing latent semantic indexing and re-ranking these results using the classifier gives better MRR at top 3 than information retrieval techniques. Its performance is comparable (if not better) to most state-of-the-art QA systems across domains.
References
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Keywords
Its performance is comparable (if not better) to most state-of-the-art QA systems across domains.