In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is to connect two words (w 1 and w 2 ) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w 1 + r ≈ w 2 in the low-dimensional space, where the multihop dependency paths are treated as a sequence of grammatical relations and modeled by a recurrent neural network. Then, we design the embedding features that consider linear context and dependency context information, for the conditional random field (CRF) based aspect term extraction. Experimental results on the SemEval datasets show that, (1) with only embedding features, we can achieve state-of-the-art results; (2) our embedding method which incorporates the syntactic information among words yields better performance than other representative ones in aspect term extraction.
For example, Yin et al. REF designed a word embedding method that considers not only the linear context but also the dependency context information.
Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction
ArXiv
ArXiv
Computer Science