Factorizing complex models

Three methods of assigning related tags in the mention detection and recognition project, with the goal of identifying named, nominal or pronominal references to real-world entities and labeling them with three types of information: entity type, entity subtype and mention type are investigated.

Abstract

As natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristics, more linguistic properties, deeper semantic and syntactic features. One such example, explored in this article, is the mention detection and recognition task in the Automatic Content Extraction project, with the goal of identifying named, nominal or pronominal references to real-world entities---mentions---and labeling them with three types of information: entity type, entity subtype and mention type. In this article, we investigate three methods of assigning these related tags and compare them on several data sets. A system based on the methods presented in this article participated and ranked very competitively in the ACE'04 evaluation.