Paper: Kernels on Linguistic Structures for Answer Extraction

ACL ID P08-2029
Title Kernels on Linguistic Structures for Answer Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

Natural Language Processing (NLP) for Infor- mation Retrieval has always been an interest- ing and challenging research area. Despite the high expectations, most of the results indicate that successfully using NLP is very complex. In this paper, we show how Support Vector Machines along with kernel functions can ef- fectively represent syntax and semantics. Our experiments on question/answer classification show that the above models highly improve on bag-of-words on a TREC dataset.