【摘 要】
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This paper describes our SeemGo system for the task of Aspect Based Sentiment Analysis in SemEval-2014.The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditi
【出 处】
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第七届国际博士生论坛暨青年教师发展研讨会
论文部分内容阅读
This paper describes our SeemGo system for the task of Aspect Based Sentiment Analysis in SemEval-2014.The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that obtains the F-score of 0.683 for Laptops and 0.791 for Restaurants by exploiting both word-based features and context features.The other three subtasks are solved by the Maximum Entropy model,with the occurrence counts of unigram and bigram words of each sentence as features.
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