PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

Chenguang Wang; Ming Zhang; Yichun Yin

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PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

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arxiv 1911.03785 v2 pith:M5CGPIZB submitted 2019-11-09 cs.CL

PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

Yichun Yin , Chenguang Wang , Ming Zhang This is my paper

classification cs.CL

keywords contextdependencyaspectembeddingextractionpositionaltermword

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Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term extraction. Specifically, the positional context is modeled via relative position encoding. Besides, we enhance the dependency context by integrating more lexical information (e.g., POS tags) along dependency paths. Experiments on SemEval 2014/2015/2016 datasets show that our approach outperforms other embedding methods in aspect term extraction.

PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

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