Invention Grant
- Patent Title: Joint learning of local and global features for entity linking via neural networks
-
Application No.: US16840846Application Date: 2020-04-06
-
Publication No.: US11755885B2Publication Date: 2023-09-12
- Inventor: Nicolas R. Fauceglia , Alfio M. Gliozzo , Oktie Hassanzadeh , Thien H. Nguyen , Mariano Rodriguez Muro , Mohammad Sadoghi Hamedani
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Scully, Scott, Murphy & Presser, P.C.
- Agent Caleb Wilkes
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/045 ; G06N3/044 ; G06N3/084

Abstract:
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.
Public/Granted literature
- US20200234102A1 JOINT LEARNING OF LOCAL AND GLOBAL FEATURES FOR ENTITY LINKING VIA NEURAL NETWORKS Public/Granted day:2020-07-23
Information query