| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === Recently, the fake news problem becomes more and more serious because anyone can tweet or post anything with the click of a mouse. To solve the fake news problem, the first step is to understand the content of the posts or articles which is the purpose of the stance classification task. The goal of stance classification is to understand the two given inputs and determine the relation between the stance of them. One of the given inputs is a target claim, which is a statement about a certain target. The other is a headline or an article that agrees with, opposes to, or discusses the target claim. In this paper, we propose a model with the polarity classifier and attention mechanism, the Attention Model(AM) with similarity function to extract the important information from both short and long content. The experiments show that the proposed methods perform better than the baseline and competitors.
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