Product Feature Mining: Semantic Clues versus Syntactic Constituents
Liheng Xu, Kang Liu, Siwei Lai, Jun Zhao · 2014
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 syntaxbased approaches, which not only mines product features more accurately, but also extracts more infrequent product features.