Structured Named Entity Recognition by Cascading CRFs

Abstract

NER is an important task in NLP, often used as a basis for further treatments. A new challenge has emerged in the last few years: structured named entity recognition, where not only named entities must be identied but also their hierarchical components. In this article, we describe a cascading CRFs approach to address this challenge. It reaches the state of the art while remaining very simple on a structured NER challenge. We then oer an error analysis of our system based on a detailed, yet simple, error classication.

Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

“…For run 1 and 2 of EL only on English, they improved the candidate entity ranking by calculating cosine similarities between the contextual embeddings of a sentence containing the target entity mention and a modified sentence where [38] for French, [32] for German. SEM [10] is a CRF-based tool using Wapiti [28] as its linear CRF implementation. https://www.elastic.co/ the mention was replaced with a candidate entity description from Wikidata.…”