الكيانات المتعلقة بالأحداث والأحداث في النص هي مكون رئيسي لفهم اللغة الطبيعية.دقة Coreference Coreference، على وجه الخصوص، أمر مهم بالنسبة للمصلحة المتزايدة بمهام تحليل المستندات متعددة الوثائق.في هذا العمل، نقترح نموذجا جديدا يمتد نموذج التنبؤ المتسلسل الفعال لتحليل Corefery لإعدادات تبادل المستندات وتحقق نتائج تنافسية لكلا كلا كلا كائن الكيان والحدث مع توفير أدلة قوية على فعالية كل من النماذج المتسلسلة والاستدلال المرتفعإعدادات الوثيقة عبر المستندات.يتطلب نموذجنا بشكل تدريجي يذكر في تمثيل الكتلة ويتوقع الروابط بين الإشارة والمجموعات التي تم إنشاؤها بالفعل، تقريب نموذج أعلى للترتيب.بالإضافة إلى ذلك، نقوم بإجراء دراسات بديلة الأزمة التي توفر رؤى جديدة في أهمية مختلف المدخلات وأنواع التمثيل في Courceer.
Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.
References used
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Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents
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