A Comparison of Named Entity Recognition Tools Applied to Biographical Texts

TL;DR

This paper compares several publicly available NER tools on a new biographical text corpus, evaluating their performance differences and potential for combined use to enhance accuracy in recognizing entities in biographical data.

Contribution

It introduces a new annotated Wikipedia corpus for biographical texts and provides a comparative analysis of four prominent NER tools, highlighting their strengths and limitations.

Findings

Stanford NER performs best overall

Performance varies by entity type and article category

Combining tools could improve NER accuracy

Abstract

Named entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts. For this matter, we first constitute a new corpus by annotating Wikipedia articles. We then select publicly available, well known and free for research NER tools for comparison: Stanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply them to our corpus, assess their performances and compare them. When considering overall performances, a clear hierarchy emerges: Stanford has the best results, followed by LingPipe, Illionois and OpenCalais. However, a more detailed evaluation performed relatively to entity types and article categories highlights the fact their performances are diversely influenced by those factors. This complementarity opens an interesting perspective regarding the combination of these individual tools in order to improve performance.