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- W2892068161 abstract "In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states." @default.
- W2892068161 created "2018-09-27" @default.
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- W2892068161 date "2018-04-01" @default.
- W2892068161 modified "2023-10-18" @default.
- W2892068161 title "Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields" @default.
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- W2892068161 doi "https://doi.org/10.1109/icassp.2018.8462160" @default.
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