text: This paper demonstrates end-to-end neural network architectures for Vietnamese named entity recognition. Our best model is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which achieves an F1 score of 88.59 on a standard test set. Our system is able to achieve a comparable performance to the first-rank system of the VLSP campaign without using any syntactic or hand-crafted features. We also give an extensive empirical study on using common deep learning models for Vietnamese NER, at both word and character level.
  @cite_5: This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based learning, and hidden Markov model) are combined under different conditions. When no gazetteer or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the F-measure error by a factor of 15 to 21 on the English data.
  @cite_14: 
  @cite_22: We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.
summary: Within the large body of research on NER which have been published in the last two decades, we identify two main approaches. The first approach is characterized by the use of well-established sequence labeling models such as conditional random field (CRF), hidden markov model, support vector machine, maximum entropy and so on. The performance of these models is heavily dependent on hand-crafted features. In particular, most of the participants at CoNLL-2003 shared task attempted to use information other than the available training data such as gazetteers and unannotated data. The best system at CoNLL-2003 shared task is the work of @cite_5 which achieved an @math score of 88.76 them by using phrase features extracted from an external database. Moreover, training NER models jointly with related tasks helps improve their performance. For instance, @cite_22 trained a CRF model for joint-learning three tasks, including coreference resolution, entity linking, and NER, and achieved the state-of-the-art result on OntoNotes dataset. With a similar approach, @cite_14 gained the best performance on CoNLL-2003 shared task dataset.