Communication Dans Un Congrès Année : 2015

A Unified Kernel Approach For Learning Typed Sentence Rewritings

Résumé

Many high level natural language processing problems can be framed as determining if two given sentences are a rewriting of each other. In this paper, we propose a class of kernel functions, referred to as type-enriched string rewriting kernels, which, used in kernel-based machine learning algorithms, allow to learn sentence rewritings. Unlike previous work, this method can be fed external lexical semantic relations to capture a wider class of rewriting rules. It also does not assume preliminary syntactic parsing but is still able to provide a unified framework to capture syntactic structure and alignments between the two sentences. We experiment on three different natural sentence rewriting tasks and obtain state-of-the-art results for all of them.

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hal-02281919 , version 1 (09-09-2019)

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Martin Gleize, Brigitte Grau. A Unified Kernel Approach For Learning Typed Sentence Rewritings. Annual Meeting of the Association for Computational Linguistics, The Association for Computer Linguistics, Jan 2015, Beijing, China. pp.939 - 949, ⟨10.3115/v1/P15-1091⟩. ⟨hal-02281919⟩
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