Papers by Kathy Lee
Detecting Gang-Involved Escalation on Social Media Using Context (D18-1)
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Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown
| Challenge: | In cities such as Chicago, gang-involved youth have increasingly turned to social media to post about their experiences and intents online. |
| Approach: | They propose a system that uses domain-specific resources and contextual representations of the emotional and semantic content of the user’s recent tweets and their interactions with other users to detect Aggression and Loss in social media posts. |
| Outcome: | The proposed system improves on a large unlabeled dataset and incorporates contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
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Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |