An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing
This study provides practical insights for Vietnamese text processing, but it is incremental as it applies existing methods to a specific language domain.
The paper empirically compares Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs) for Vietnamese part-of-speech tagging and named entity recognition, achieving 90.65% and 86.03% accuracy with simple features and showing that LSTMs do not always outperform CRFs on moderate-sized datasets.
This paper presents an empirical study of two widely-used sequence prediction models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs), on two fundamental tasks for Vietnamese text processing, including part-of-speech tagging and named entity recognition. We show that a strong lower bound for labeling accuracy can be obtained by relying only on simple word-based features with minimal hand-crafted feature engineering, of 90.65\% and 86.03\% performance scores on the standard test sets for the two tasks respectively. In particular, we demonstrate empirically the surprising efficiency of word embeddings in both of the two tasks, with both of the two models. We point out that the state-of-the-art LSTMs model does not always outperform significantly the traditional CRFs model, especially on moderate-sized data sets. Finally, we give some suggestions and discussions for efficient use of sequence labeling models in practical applications.