NDLI: Recursive Autoencoder with HowNet Lexicon for Sentence-Level Sentiment Analysis

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Recursive Autoencoder with HowNet Lexicon for Sentence-Level Sentiment Analysis

Content Provider	ACM Digital Library
Author	Fu, Xianghua Xu, Yingying
Abstract	Semantic word representations have been very useful but usually ignore the syntactic relationship. In the task of sentiment analysis, compositional vector representations require more structure information from natural language text and richer supervised training for more accuracy predictions. However, labeled data are generally expensive to acquire in reality. To remedy this, we propose a new method that train our model based on fully labeled parse tree using supervised learning without manual annotation. Our method not only significantly reduces the burden of manual labeling, but also allows the compositionality to capture syntactic and semantic information jointly. We show the effectiveness of this model on the task of sentence-level sentiment classification and conduct preliminary experiments to investigate its performance. Lastly, it can accurately predict the sentiment distribution and outperforms other approaches.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450337359
DOI	10.1145/2818869.2818908
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep learning Sentiment analysis Sentiment label Hownet lexicon Word embedding Data mining Parse tree
Content Type	Text
Resource Type	Article

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