  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.
