Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning
المؤلفون المشاركون
Zhang, Hongjun
Feng, Yuntian
Hao, Wenning
Chen, Gang
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-08-14
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts.
For reinforcement learning, we model the task as a two-step decision process.
Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process.
By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously.
Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction.
On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process.
Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process.
Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process.
Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Feng, Yuntian& Zhang, Hongjun& Hao, Wenning& Chen, Gang. 2017. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141104
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Feng, Yuntian…[et al.]. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1141104
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Feng, Yuntian& Zhang, Hongjun& Hao, Wenning& Chen, Gang. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141104
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1141104
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر