Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling

NAACL 2019  ·  Meishan Zhang, Peili Liang, Guohong Fu ·

Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient for a given predicate. As predicate agents and patients usually correspond to opinion holders and targets respectively, SRL could be valuable for ORL. In this work, we propose a simple and novel method to enhance ORL by utilizing SRL, presenting semantic-aware word representations which are learned from SRL. The representations are then fed into a baseline neural ORL model as basic inputs. We verify the proposed method on a benchmark MPQA corpus. Experimental results show that the proposed method is highly effective. In addition, we compare the method with two representative methods of SRL integration as well, finding that our method can outperform the two methods significantly, achieving 1.47{\%} higher F-scores than the better one.

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Results from the Paper

 Ranked #1 on Fine-Grained Opinion Analysis on MPQA (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Fine-Grained Opinion Analysis MPQA SRL-SAWR Holder Binary F1 84.91 # 1
Target Binary F1 73.29 # 1


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