Papers by Hiroki Okamoto

2 papers
Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)

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Challenge: opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy .
Approach: They propose a model that handles context in many natural language processing areas to solve a problem of extracting opinion references from text.
Outcome: Experiments on tweets that refer to television programs show the proposed model can extract opinion references with more than 90% accuracy.
Label Embedding using Hierarchical Structure of Labels for Twitter Classification (D19-1)

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Challenge: Twitter is used for disaster monitoring and news material gathering . we propose a method that can consider the hierarchical structure of labels and labels themselves .
Approach: They propose a method that can consider the hierarchical structure of labels and label texts themselves.
Outcome: The proposed method outperforms the methods of the conference participants over the text REtrieval Conference (TREC) 2018 Incident Streams (IS) dataset.

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