Seminar: Prof. Yulan He, "Emotion Ranking and Emotion Cause Detection from Text"

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Title: Emotion Ranking and Emotion Cause Detection from Text


Text might contain or evoke multiple emotions with varying intensities. Traditional approaches typically cast the problem of detecting multiple emotions from text as multi-label classification. This talk will first present a novel ranking-based approach to generate a ranked list of relevant emotions where top-ranked emotions are more intensely associated with text compared to lower ranked emotions. Furthermore, since emotions might be evoked by different hidden topics, it is important to unveil and incorporate such topical information to understand how the emotions are evoked. A novel neural network approach with topical information incorporated will be discussed for relevant emotion ranking. Finally, the problem of identifying the reasons behind a certain emotion expressed in text can be framed as a reading comprehension task and a new approach based on memory networks will be presented for emotion cause detection from text.


Yulan He is a Professor in the Department of Computer Science at the University of Warwick, UK. She obtained her PhD degree in spoken language understanding from the University of Cambridge. Yulan is experienced in statistical modelling and text mining, particularly the integration of machine learning and natural language processing for text understanding. She has published over 150 papers on topics including sentiment analysis, information extraction, clinical text mining, recommender systems, learning analytics and spoken dialogue systems. In the past, she has served as an Area Chair in Sentiment Analysis in top natural language processing conferences including ACL, EMNLP and NAACL.

Organisational information:

This particular meetup is part of a seminar series on Natural Language Processing funded by the Centre for Communication and Computing (CCC) at the University of Copenhagen. There will be refreshments from 16:30, the talk will start at 17:00.