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Speech-Act Analysis System Based on Dialogue Level RNN-CNN Effective on the Exposure Bias Problem
http://doi.org/10.5626/JOK.2018.45.9.911
The speech-act is the intention of the speaker in his or her utterance. Speech-act analysis classifies the speech-act about a given utterance. Recently, a lot of research based on machine learning using a corpus have been done. We have two goals in this study. First, the utterances in dialogue are continuative and organically related to each other, and the speech-act of a current utterance is greatly influenced by the direct previous utterance. Second, previous research did not deal with the exposure bias problem when the speech-act analysis model use the speech-act result of a previous utterance. In this paper, we suggest the RNN-CNN dialogue-level speech-act analysis model. We also experiment with the exposure bias problem. Finally, the RNN-CNN shows an 86.87% performance on the oracle condition and an 86.27% performance on the greedy condition.
A Method to Solve the Entity Linking Ambiguity and NIL Entity Recognition for efficient Entity Linking based on Wikipedia
Hokyung Lee, Jaehyun An, Jeongmin Yoon, Kyoungman Bae, Youngjoong Ko
http://doi.org/10.5626/JOK.2017.44.8.813
Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user’s query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.
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