Discriminative Phrase Embedding for Paraphrase Identification

April 02, 2016 ยท Declared Dead ยท ๐Ÿ› North American Chapter of the Association for Computational Linguistics

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Authors Wenpeng Yin, Hinrich Schรผtze arXiv ID 1604.00503 Category cs.CL: Computation & Language Citations 40 Venue North American Chapter of the Association for Computational Linguistics Last Checked 4 months ago
Abstract
This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effectively. Based on these two innovations we get competitive state-of-the-art performance on paraphrase identification.
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