PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
This work addresses aspect term extraction for natural language processing applications, presenting an incremental improvement over existing dependency-based embeddings.
The paper tackled aspect term extraction by proposing PoD, a word embedding method that incorporates both dependency and positional contexts, achieving superior performance on SemEval 2014/2015/2016 datasets compared to other embedding methods.
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.