Multilingual Opinion Factor Extraction Fusing Aspect Semantics and Grid Tagging
Aspect-oriented fine-grained opinion extraction(AFOE)aims to extract the aspect and opinion terms in the reviews in the form of opinion pairs or to extract the sentiment polarity to form opinion triplets.Previous studies usually extract opinion fac-tors in a pipeline manner,which is prone to the problem of error propagation,most of them only focus on the single sub-task ex-traction of aspect terms or opinion terms,and ignore the mutually interactive and indicative information between different opinion factors,which lead to the problem that opinion excavation tasks are incomplete.In addition,the existing researches do not pay at-tention to the research of Chinese-oriented opinion factors extraction.To tackle the problems,this paper proposes multilingual opinion factors extraction model fusing aspect semantics and grid tagging.Firstly,inward LSTM(Inward-LSTM)and outward LSTM(Outward-LSTM)are exploited to encode aspect terms and corresponding left-right contexts to establish the association between aspect and candidate opinion terms,and then combine global context information to generate contextualized representa-tion of specific aspect semantic features,which is beneficial to improve the performance of downstream opinion factors extraction.Secondly,the inference strategy of the grid tagging scheme is applied to decode the potential indications between aspect and opi-nion terms for more accurate extraction,the AFOE task is handled in an end-to-end manner.Compared with the baseline model,the F1 scores of the proposed model in the Chinese and English datasets increase by 0.89％～4.11％for the aspect opinion pair extraction task,and 1.36％～3.11％for the triplet extraction task.Experimental results show that the improved model can effec-tively extract the opinion factors of Chinese and English comments,the performance is significantly better than the baseline model.
NETL
NSTL
万方数据
