ATTITUDE CLASSIFICATION IN ADJACENCY PAIRS OF A HUMAN-AGENT INTERACTION WITH HIDDEN CONDITIONAL RANDOM FIELDS

被引：0

作者：

Barriere, Valentin ^{[1
]}

Clavel, Chloe ^{[1
]}

Essid, Slim ^{[1
]}

机构：

[1] Univ Paris Saclay, Telecom ParisTech, LTCI, F-75013 Paris, France

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年

关键词：

Hidden Conditional Random Field; Opinion Mining; Linguistic Patterns; Attitude Detection;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：4949 / 4953

页数：5

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