Applying a finite automata acquisition algorithm to named entity recognition

Abstract

In this work, Causal-State Splitting Reconstruction algorithm, originally conceived to model stationary processes by learning finite state automata from data sequences, is for the first time applied to NLP tasks, namely Named Entity Recognition. The obtained results are slightly below the best systems presented in CoNLL 2002 shared task, though given the simplicity of the used features, they are really promising. Once the viability of using this algorithm for NLP tasks is stated, we plan to improve the results obtained at NER task, as well as to apply it to other NLP sequence recognition tasks such as PoS tagging, chunking, subcategorization patterns acquisition, etc.