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In this paper, we develop a novel approach to aspect term extraction based onunsupervised learning of distributed representations of words and dependencypaths. The basic idea is to connect two words (w1 and w2) with the dependencypath (r) between them in the embedding space. Specifically, our methodoptimizes the objective w1 + r = w2 in the low-dimensional space, where themulti-hop dependency paths are treated as a sequence of grammatical relationsand modeled by a recurrent neural network. Then, we design the embeddingfeatures that consider linear context and dependency context information, forthe conditional random field (CRF) based aspect term extraction. Experimentalresults on the SemEval datasets show that, (1) with only embedding features, wecan achieve state-of-the-art results; (2) our embedding method whichincorporates the syntactic information among words yields better performancethan other representative ones in aspect term extraction.

Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction