发明申请
- 专利标题: JOINT LEARNING OF LOCAL AND GLOBAL FEATURES FOR ENTITY LINKING VIA NEURAL NETWORKS
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申请号: US15351897申请日: 2016-11-15
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公开(公告)号: US20180137404A1公开(公告)日: 2018-05-17
- 发明人: Nicolas R. Fauceglia , Alfio M. Gliozzo , Oktie Hassanzadeh , Thien H. Nguyen , Mariano Rodriguez Muro , Mohammad Sadoghi Hamedani
- 申请人: International Business Machines Corporation
- 主分类号: G06N3/04
- IPC分类号: G06N3/04 ; G06N3/08
摘要:
A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.
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