Joint Mention Extraction and Classification with Mention Hypergraphs 论文

摘要

We present a novel model for the task of joint mention extraction and classifi-cation. Unlike existing approaches, our model is able to effectively capture over-lapping mentions with unbounded lengths. The model is highly scalable, with a time complexity that is linear in the number of words in the input sentence and linear in the number of possible mention classes. Our model can be extended to additionally capture mention heads explicitly in a joint manner under the same time complexity. We demonstrate the effectiveness of our model through extensive experiments on standard datasets. 1