Abstract：Implicit discourse relation recognition is an important subtask in the discourse analysis field. Most existing studies assume the balance between the numbers of positive and negative samples, and employ random under-sampling method to keep the training data well balanced. However, the training data has imbalanced distribution in reality which affect the recognition performance of the implicit discourse relation. To solve this problem, we propose a novel implicit discourse relation recognition method based on the frame semantic vectors. Firstly, we represent the argument as a frame semantic vector using the FrameNet resource, and then mine a number of effective discourse relation samples from the external data resources based on this new representation. Finally, we add the mined samples into the origin training data sets and perform experiment on this extended data sets. Evaluation on the Penn Discourse Treebank (PDTB) show that the proposed method perform better than the current mainstream imbalanced classification methods. Key words implicit discourse recognition; imbalanced data; frame semantic vectors
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