Title :
Robust tree-structured Named Entities Recognition from speech
Author :
Raymond, Christian
Author_Institution :
IRISA, INSA de Rennes, Rennes, France
Abstract :
Named Entity Recognition is a well-known Natural Language Processing (NLP) task, used as a preliminary processing to provide a semantic level to more complex tasks. Recently a new set of named entities has been defined; this set has a multilevel tree structure, where base entities are combined to define more complex ones. In this paper I describe an effective and original NER system robust to noisy speech inputs that ranked first at the 2012 ETAPE NER evaluation campaign with results far better than those of the other participating systems.
Keywords :
learning (artificial intelligence); natural language processing; speech recognition; trees (mathematics); 2012 ETAPE NER evaluation campaign; NLP task; multilevel tree structure; named entity recognition; natural language processing task; speech inputs; Data mining; Feature extraction; Natural language processing; Noise measurement; Robustness; Speech; Speech recognition; Conditional Random Fields; discretization of numeric features; named entities recognition;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6639319
