Entity Mention Detection using a Combination of Redundancy-Driven Classifiers

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 being 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 being used to annotate corpora, such as Italian Wikipedia, thus providing easy access to syntactic and semantic annota...

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Silvana Marianela Bernaola Biggio, Manuela Speranza, Roberto Zanoli
• Keywords: Education, Entity Mention Detection, Large Text Corpus, LREC 2010, Unannotated Corpus