Models Cascade for Tree-Structured Named Entity Detection
Marco Dinarelli, Sophie Rosset · International Joint Conference on Natural Language Processing · 2011
Named Entity Recognition (NER) is a well-known Natural Language Processing (NLP) task, used as a preliminary processing to provide a semantic level to more complex tasks. In this paper we describe a new set of named entities having a multilevel tree structure, where base entities are combined to define more complex ones. This definition makes the NER task more complex than previous tasks, even more due to the use of noisy data for the annotation: transcriptions of French broadcast data. We propose an original and effective system to tackle this new task, putting together the strengths of solutions for sequence labeling approaches and syntactic parsing via cascading of different models. Our system was evaluated in the 2011 Quaero named entity detection evaluation campaign and ranked first, with results far better than those of the other participating systems.