Neural Cross-Lingual Entity Linking

Abstract

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.

Association for the Advancement of Artificial Intelligence (AAAI), 2018

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• Controllable Text Simplification with Explicit Paraphrasing
• On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
• Entity Linking via Dual and Cross-Attention Encoders
• Towards Zero-resource Cross-lingual Entity Linking
• Joint Neural Entity Disambiguation with Output Space Search
• Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
• Contextualized End-to-End Neural Entity Linking
• Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
• Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment
• Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment