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 (w1 and w2) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w1 + r = w2 in the low-dimensional space, where the multi-hop 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.

本文针对方面术语提取，基于无监督学习的分布式单词和依赖路径表示提出了一种新颖的方法，使用递归神经网络建模序列化的语法关系，以单词之间的依赖句法关系进行词嵌入训练，并使用状态转移条件随机场(score function)来完成特定领域术语的提取。通过 SemEval 数据集上的实验，结果表明，我们的方法在方面术语提取方面取得了最好的性能表现。

无监督的词汇和依存路径嵌入在方面术语提取中的应用