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Register a new userTreasures Outside Contexts: Improving Event Detection via Global Statistics
الكنوز خارج السياقات: تحسين اكتشاف الحدث عبر الإحصاءات العالمية
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يهدف اكتشاف الحدث (ED) إلى تحديد مثيلات الأحداث من الأنواع المحددة في نصوص معينة، والتي تم إضفاء الطابع الرسمي على أنها مهمة تسلسل تسلسل.بقدر ما نعلم، تتخذ نماذج إد القائمة القائم على العصبي القرارات التي تعتمد تماما على الميزات الدلالية السياقية لكل كلمة في النص المدبأ، والتي نجدها من السهل أن تكون من السهل الخلط بين السياقات المتنوعة في مرحلة الاختبار.تحقيقا لهذه الغاية، وصلنا إلى فكرة إدخال مجموعة من الميزات الإحصائية من ترددات حدوث حدث Word-Event في مجموعة التدريب بأكملها للتعاون مع ميزات السياقية.على وجه التحديد، نقترح شبكة تمييزية دلالية وإحصائية مشتركة (SS-JDN) تتكون من مستخرج ميزة دلالية، واستخراج ميزة إحصائية، وتمييز حدث مشترك.في التجارب، يتجاوز SS-JDN بفعالية عشرة خطوط أساسية قوية حديثة على مجموعة بيانات ACE2005 و KBP2015.علاوة على ذلك، نحن نقوم بإجراء تجارب واسعة لتحقيق SS-JDN بشكل شامل.
Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying entirely on the contextual semantic features of each word in the inputted text, which we find is easy to be confused by the varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (SS-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, SS-JDN effectively exceeds ten recent strong baselines on ACE2005 and KBP2015 datasets. Further, we perform extensive experiments to comprehensively probe SS-JDN.
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Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturi
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