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Leveraging Psycholinguistic Resources and Emotional Sequence Models for Suicide Note Emotion Annotation
並列摘要
We describe the submission entered by SRI International and UC Davis for the I2B2 NLP Challenge Track 2. Our system is based on a machine learning approach and employs a combination of lexical, syntactic, and psycholinguistic features. In addition, we model the sequence and locations of occurrence of emotions found in the notes. We discuss the effect of these features on the emotion annotation task, as well as the nature of the notes themselves. We also explore the use of bootstrapping to help account for what appeared to be annotator fatigue in the data. We conclude a discussion of future avenues for improving the approach for this task, and also discuss how annotations at the word span level may be more appropriate for this task than annotations at the sentence level.
延伸閱讀
- Liakata, M., Kim, J. Y., Saha, S., astings, J., & Schuhmann, D. R. (2012). Three Hybrid Classifiers for the Detection of Emotions in Suicide Notes. Biomedical Informatics Insights, (), 175-184. https://doi.org/10.4137/BII.S8967
- Roberts, K., & Harabagiu, S. M. (2012). Statistical and Similarity Methods for Classifying Emotion in Suicide Notes. Biomedical Informatics Insights, (), 195-204. https://doi.org/10.4137/BII.S8958
- Cherry, C., Mohammad, S. M., & Bruijn, B. D. (2012). Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes. Biomedical Informatics Insights, (), 147-154. https://doi.org/10.4137/BII.S8933
- Sohn, S., Torii, M., Li, D., Wagholikar, K., Wu, S., & Liu, H. (2012). A Hybrid Approach to Sentiment Sentence Classification in Suicide Notes. Biomedical Informatics Insights, (), 43-50. https://doi.org/10.4137/BII.S8961
- Read, J., Velldal, E., & Øvrelid, L. (2012). Labeling Emotions in Suicide Notes: Cost-Sensitive Learning with Heterogeneous Features. Biomedical Informatics Insights, (), 99-103. https://doi.org/10.4137/BII.S8930