Discriminative learning for joint template filling

Minkov Einat, Zettlemoyer Luke

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

This paper presents a joint model for template filling, where the goal is to automatically specify the fields of target relations such as seminar announcements or corporate acquisition events. The approach models mention detection, unification and field extraction in a flexible, feature-rich model that allows for joint modeling of interdependencies at all levels and across fields. Such an approach can, for example, learn likely event durations and the fact that start times should come before end times. While the joint inference space is large, we demonstrate effective learning with a Perceptron-style approach that uses simple, greedy beam decoding. Empirical results in two benchmark domains demonstrate consistently strong performance on both mention detection and template filling tasks.

اللغة الأصليةإنجليزيّة أمريكيّة
عنوان منشور المضيف50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
الصفحات845-853
عدد الصفحات9
حالة النشرنُشِر - 2012
الحدث50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Jeju Island, كوريا الجنوبيّة
المدة: ٨ يوليو ٢٠١٢١٤ يوليو ٢٠١٢

سلسلة المنشورات

الاسم50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
مستوى الصوت1

!!Conference

!!Conference50th Annual Meeting of the Association for Computational Linguistics, ACL 2012
الدولة/الإقليمكوريا الجنوبيّة
المدينةJeju Island
المدة٨/٠٧/١٢١٤/٠٧/١٢

All Science Journal Classification (ASJC) codes

  • !!Computational Theory and Mathematics
  • !!Software

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