Kernels on Linguistic Structures for Answer Extraction
نویسندگان
چکیده
Natural Language Processing (NLP) for Information Retrieval has always been an interesting 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 effectively 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.
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