Learning relatedness measures for entity linking

Abstract

Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowledge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant entities selected for annotation, since this minimizes errors in disambiguating entity-linking.The definition of an effective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of different state-of-the-art entity-linking algorithms.

Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding

“…To evaluate the quality of entity embeddings, we conduct experiments using the dataset which is designed for measuring entity relatedness (Ceccarelli et al, 2013;Huang et al, 2015;Yamada et al, 2016). The dataset contains 3,314 entities, and each mention has 91 candidate entities on average with gold-standard labels indicating whether they are semantically related.…”

Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

“…To test the quality of the vector representation of entities, we conducted an experiment using a dataset for entity relatedness created by Ceccarelli et al (Ceccarelli et al, 2013). The dataset consists of training, test, and validation sets, and we only use the test set for this experiment.…”

Twitter anticipates bursts of requests for Wikipedia articles

“…[5], while Milne and Witten [6] largely improved this first solution. Since entity relatedness has been recognized as the most important feature to disambiguate entity-linking, Ceccarelli et al [1] discuss how an effective relatedness measure can be learnt from large training sets using a learning-to-rank approach.…”