TAMU at KBP 2017: Event Nugget Detection and Coreference Resolution
This work addresses event extraction for natural language processing applications, but it is incremental as it builds on existing methods with minimal features.
The paper tackled event nugget detection and coreference resolution by building a system based on syntactic features and joint modeling, achieving micro-average F1 scores of 57.72 for span detection, 44.27 for type identification, and 42.47 for realis status classification, with a CoNLL F1 score of 27.20 for coreference resolution.
In this paper, we describe TAMU's system submitted to the TAC KBP 2017 event nugget detection and coreference resolution task. Our system builds on the statistical and empirical observations made on training and development data. We found that modifiers of event nuggets tend to have unique syntactic distribution. Their parts-of-speech tags and dependency relations provides them essential characteristics that are useful in identifying their span and also defining their types and realis status. We further found that the joint modeling of event span detection and realis status identification performs better than the individual models for both tasks. Our simple system designed using minimal features achieved the micro-average F1 scores of 57.72, 44.27 and 42.47 for event span detection, type identification and realis status classification tasks respectively. Also, our system achieved the CoNLL F1 score of 27.20 in event coreference resolution task.