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Register a new userDocument-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies
استخراج الأحداث على مستوى المستند مع التعلم الفعال نهاية إلى نهاية التبعيات عبر الأحداث
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غالبا ما يتطلب فهم الروايات بالكامل من الأحداث في سياق المستندات بأكملها ونمذجة علاقات الحدث.ومع ذلك، فإن استخراج الأحداث على مستوى المستند هو مهمة صعبة لأنها تتطلب استخراج الحدث والكيان الأساسية، والتقاط الحجج التي تمتد عبر جمل مختلفة.تعمل الأعمال الموجودة على استخراج الأحداث عادة على استخراج الأحداث من جمل واحدة، والتي تفشل في التقاط العلاقات بين الحدث تذكر على نطاق المستند، وكذلك حجج الحدث التي تظهر في جملة مختلفة عن مشغل الحدث.في هذه الورقة، نقترح نماذج طراز نهاية إلى نهاية شبكات القيمة العميقة (DVN)، خوارزمية التنبؤ منظم، لالتقاط التبعيات عبر الأحداث بكفاءة لاستخراج الأحداث على مستوى المستند.تظهر النتائج التجريبية أن نهجنا يحقق أداء قابلا للمقارنة مع النماذج القائمة على CRF على ACE05، بينما تتمتع بكفاءة حسابية أعلى بكثير.
Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.
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