Product Feature Mining: Semantic Clues versus Syntactic Constituents

Abstract

Product feature mining is a key subtask in fine-grained opinion mining. Previous works often use syntax constituents in this task. However, syntax-based methods can only use discrete contextual information, which may suffer from data sparsity. This paper proposes a novel product feature mining method which leverages lexical and contextual semantic clues. Lexical semantic clue verifies whether a candidate term is related to the target product, and contextual semantic clue serves as a soft pattern miner to find candidates, which exploits semantics of each word in context so as to alleviate the data sparsity problem. We build a semantic similarity graph to encode lexical semantic clue, and employ a convolutional neural model to capture contextual semantic clue. Then Label Propagation is applied to combine both semantic clues. Experimental results show that our semantics-based method significantly outperforms conventional syntax-based approaches, which not only mines product features more accurately, but also extracts more infrequent product features.