Neural Networks Models for Entity Discovery and Linking

2016/11/11 by Dan Liu, Wei Lin, Liu, Dan +7
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1611.03558

openalex publication_date 2016/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.

Citations

• ADADELTA: An Adaptive Learning Rate Method
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

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