Seminar: "Unsupervised Ranking of Knowledge Bases for Named Entity Recognition", Yassine M'rabet, NLM, USA

Date : 1 September 2016

Speaker: Yassine Mrabet, Lister Hill National Center for Biomedical Communications, National Library of Medicine, USA

Unsupervised Ranking of Knowledge Bases for Named Entity Recognition

Abstract:

With the continuous growth of freely accessible knowledge bases and the heterogeneity of textual corpora, selecting the most adequate knowledge base for named entity recognition is becoming a challenge in itself. In this talk, we will present an unsupervised method to rank knowledge bases according to their adequacy for the recognition of named entities in a given corpus. Building on a state-of-the-art, unsupervised entity linking approach, we propose several evaluation metrics to measure the lexical and structural adequacy of a knowledge base for a given corpus. We study the correlation between these metrics and three standard performance measures: precision, recall and F1 score. Our multi-domain experiments on 9 different corpora with 6 knowledge bases show that three of the proposed metrics are strong performance predictors having 0.62 to 0.76 Pearson correlation with precision and 0.96 correlation with both recall and F1 score.

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