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- W4200152311 abstract "Emotion recognition in conversations is an important step in various virtual chatbots which require opinion-based feedback, like in social media threads, online support, and many more applications. Current emotion recognition in conversations models face issues like: (a) loss of contextual information in between two dialogues of a conversation, (b) failure to give appropriate importance to significant tokens in each utterance, (c) inability to pass on the emotional information from previous utterances. The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by performing unique feature extraction using knowledge graphs, sentiment lexicons and phrases of natural language at all levels (word and position embedding) of the utterances. Experiments on emotion recognition in conversations datasets show that AdCOFE is beneficial in capturing emotions in conversations." @default.
- W4200152311 created "2021-12-31" @default.
- W4200152311 date "2021-12-09" @default.
- W4200152311 modified "2023-09-27" @default.
- W4200152311 title "Table 3: Comparison of performance of proposed model with baseline models in terms of accuracy and F1-score." @default.
- W4200152311 doi "https://doi.org/10.7717/peerj-cs.786/table-3" @default.
- W4200152311 hasPublicationYear "2021" @default.
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