- Junpei Zhou, Xinyu Wang, Po-yao Huang, Alexander Hauptmann. 2019. CMU-Informedia at TREC 2019 Incident Streams Track. Abstract: We describe CMU-Informedia’s models for the TREC 2019 Incident Streams track. The goal of this track is classifying event/incident related tweets by High-level Information Types such as ‘SearchAndRescue’, ‘InformationWanted’ and so on. Each tweet should be assigned as many categories as are appropriate. What’s more, this track requires predicting the Importance Scores, which is converted from the Importance Labels including ‘Critical’, ‘High’, ‘Medium’, ‘Low’ and ‘Irrelevant’. For predicting the information types, we use feature extractors to extract features including meta-information, user entity, and textual embeddings, and then we build an information type predictor on those features. For predicting the importance scores, we build an importance score predictor which combines the scores derived from the predicted information types and the scores produced by a regression model. Evaluation results show that our models perform well on all metrics, and different models perform particularly well on different aspects.
