Sophia Lee


2016

pdf
A Bilingual Attention Network for Code-switched Emotion Prediction
Zhongqing Wang | Yue Zhang | Sophia Lee | Shoushan Li | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.

2015

pdf
Emotion in Code-switching Texts: Corpus Construction and Analysis
Sophia Lee | Zhongqing Wang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

pdf
Emotion Detection in Code-switching Texts via Bilingual and Sentimental Information
Zhongqing Wang | Sophia Lee | Shoushan Li | Guodong Zhou
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

pdf
Annotating Events in an Emotion Corpus
Sophia Lee | Shoushan Li | Chu-Ren Huang
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents the development of a Chinese event-based emotion corpus. It specifically describes the corpus design, collection and annotation. The proposed annotation scheme provides a consistent way of identifying some emotion-associated events (namely pre-events and post-events). Corpus data show that there are significant interactions between emotions and pre-events as well as that of between emotion and post-events. We believe that emotion as a pivot event underlies an innovative approach towards a linguistic model of emotion as well as automatic emotion detection and classification.