Bootstrapping and Rule-Based Model for Recognizing Vietnamese Named Entity

Trung, Hieu Le; Anh, Vu Le; Trung, Kien Le

doi:10.1007/978-3-319-05458-2_18

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Some other works proposed a rule-based method [16], employed bootstrapping algorithm and a rule-based model [24], combined linguistically motivated and ontological features [17] for NER.…”

Section: Related Workmentioning

confidence: 99%

Named Entity Recognition in Vietnamese Tweets

Nguyen

Snášel

2015

Computational Social Networks

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Abstract. Named entity recognition (NER) is a task of detecting named entities in documents and categorizing them to predefined classes such as Person (PER), Location (LOC), Organization (ORG) and so on. There have been many approaches proposed to tackle this problem in both formal texts such as news or authorized web content and short texts such as contents in online social network. However, those texts were written in languages other than Vietnamese. In this paper, we propose a method for NER in Vietnamese tweets. Since tweets on Twitter are noisy, irregular, short and consist of acronyms, spelling errors, NER in those tweets is a challenging task. Our method firstly normalizes tweets and then applies a learning model to recognize named entities using six different types of features. We built a training set of more than 40,000 named entities, and a testing set of 2,446 named entities to evaluate our system. The experiment results show that our system achieves encouraging performance with 82.3% F1 score.

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“…Some other works proposed a rule-based method [16], employed bootstrapping algorithm and a rule-based model [24], combined linguistically motivated and ontological features [17] for NER.…”

Section: Related Workmentioning

confidence: 99%

Named Entity Recognition in Vietnamese Tweets

Nguyen

Snášel

2015

Computational Social Networks

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“…Using SVMs, Tran et al (2007) achieved 87.75% F 1 score for the task. For semisupervised learning, Pham et al (2012) achieved 90.14% F 1 score using CRFs with the generalized expectation criteria (Mann and McCallum, 2010), while Le Trung et al (2014) reported an accuracy of 95% for their system that uses bootstrapping and rule-based models.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Learning for Vietnamese Named Entity Recognition using Online Conditional Random Fields

Pham¹,

Nguyen²,

Nguyen

et al. 2015

Proceedings of the Fifth Named Entity Workshop

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We present preliminary results for the named entity recognition problem in the Vietnamese language. For this task, we build a system based on conditional random fields and address one of its challenges: how to combine labeled and unlabeled data to create a stronger system. We propose a set of features that is useful for the task and conduct experiments with different settings to show that using bootstrapping with an online learning algorithm called Margin Infused Relaxed Algorithm increases the performance of the models.

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“…(Mc-Callum and Li, 2003;Gao et al, 2005;Ritter et al, 2011). For the Vietnamese language, several authors have attempted to tackle the NER problem using both supervised and semi-supervised methods Tran et al, 2007;Nguyen et al, 2010;Pham et al, 2012;Le Trung et al, 2014). However, previous works for NER in the Vietnamese language mainly used offline supervised learning methods, where all the training data are gathered before a model is trained.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Fifth Named Entity Workshop

2015

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Bootstrapping and Rule-Based Model for Recognizing Vietnamese Named Entity

Cited by 4 publications

References 3 publications

Named Entity Recognition in Vietnamese Tweets

Named Entity Recognition in Vietnamese Tweets

Semi-supervised Learning for Vietnamese Named Entity Recognition using Online Conditional Random Fields

Proceedings of the Fifth Named Entity Workshop

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