Sequentially accumulating attention-weighted context from previously linked entities, one pass per document, improves entity-linking accuracy over joint global inference and reduces inference cost from roughly quadratic to linear in the number of mentions.
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
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abstract
Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.
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cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Learning Dynamic Context Augmentation for Global Entity Linking
Sequentially accumulating attention-weighted context from previously linked entities, one pass per document, improves entity-linking accuracy over joint global inference and reduces inference cost from roughly quadratic to linear in the number of mentions.