Entity Mention Detection using a Combination of Redundancy-Driven Classifiers

Abstract

We present an experimental framework for Entity Mention Detection in which two different classifiers are combined to exploit Data Redundancy attained through the annotation of a large text corpus, as well as a number of Patterns extracted automatically from the same corpus. In order to recognize proper name, nominal, and pronominal mentions we not only exploit the information given by mentions recognized within the corpus being annotated, but also given by mentions occurring in an external and unannotated corpus. The system was first evaluated in the Evalita 2009 evaluation campaign obtaining good results. The current version is being used in a number of applications: on the one hand, it is used in the LiveMemories project, which aims at scaling up content extraction techniques towards very large scale extraction from multimedia sources. On the other hand, it is used to annotate corpora, such as Italian Wikipedia, thus providing easy access to syntactic and semantic annotation for both the Natural Language Processing and Information Retrieval communities. Moreover a web service version of the system is available and the system is going to be integrated into the TextPro suite of NLP tools.

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