Character-based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction

Opinion target extraction (OTE) is a fundamental step for sentiment analysis and opinion summarization. We analyze the difference between Chinese and the Indo-European languages family, and reduce Chinese OTE to a character-based sequence tagging task. Then we introduce two novel features for each character by distributing POS differentially and using predefined templates over contexts and dictionaries. We further propose a character-based BiLSTM-CRF model incorporating the two feature sequences aligned with the character sequence. Experimental results on real-world consumer review datasets show that our work significantly outperforms the baseline methods for Chinese OTE.