Vietnamese transition-based dependency parsing with supertag features

Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.1109/kse.2016.7758049

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…A Model 0 supertag for a word w encodes the dependency relation and the relative position (direction) between w and its head, i.e. left (L), right (R), or no direction (ROOT) (Nguyen and Nguyen, 2016). Model 1.…”

Section: Supertag Designmentioning

confidence: 99%

Syntax-aware Neural Semantic Role Labeling with Supertags

Kasai

Friedman

Frank

et al. 2019

Proceedings of the 2019 Conference of the North

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We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish. We use a BiLSTM to tag the text with supertags extracted from dependency parses, and we feed these supertags, along with words and parts of speech, into a deep highway BiLSTM for semantic role labeling. Our model combines the strengths of earlier models that performed SRL on the basis of a full dependency parse with more recent models that use no syntactic information at all. Our local and non-ensemble model achieves state-of-the-art performance on the CoNLL 09 English and Spanish datasets. SRL models benefit from syntactic information, and we show that supertagging is a simple, powerful, and robust way to incorporate syntax into a neural SRL system.

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Section: Supertag Designmentioning

confidence: 99%

Syntax-aware Neural Semantic Role Labeling with Supertags

Kasai

Friedman

Frank

et al. 2019

Proceedings of the 2019 Conference of the North

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“…In Vietnamese, there are quite a quantity of research works on other NLP tasks such as parsing [26,27], part-of-speech [28,29], named entity recognition [30,31], sentiment analysis [16,17,18] and question answering [32,33]. However, there are no research publications on emotion recognition for Vietnamese social media texts.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, according to a survey of 810 Vietnamese people conducted by [2] in 2018, Facebook is used the most in Vietnam. Moreover, as reported by the statistic of the number of Facebook users by [25], there are 58 million Facebook users in Vietnam, ranking as the 7th country with the most users in the world. As the larger the number of the users and the more interaction between them, the richer the data.…”

Section: Checking For Mistakesmentioning

confidence: 99%

Emotion Recognition for Vietnamese Social Media Text

Ho¹,

Nguyen²,

Nguyen³

et al. 2019

Preprint

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Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with exactly 6,927 emotionannotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74%. Our corpus is available at our research website 1 .

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“…Deeplearning4j is a library written in the Java programming language and supports the development, training, and deployment of deep learning algorithms. To extract dependency relation and get POS tag of feedback, we used a Vietnamese based dependency parsing tool [19]. Two deep learning models share the same configuration.…”

Section: Experimental Settingsmentioning

confidence: 99%

Deep Learning versus Traditional Classifiers on Vietnamese Students’ Feedback Corpus

Nguyen

Hong

Nguyen

et al. 2018

2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

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Student's feedback is an important source of collecting students' opinions to improve quality of training activities. Implementing sentiment analysis into student feedback data, we can determine sentiments polarities which express all problems in the institution since changes necessary will be applied to improve the quality of teaching and learning. This study focused on the machine learning and natural language processing techniques (Naive Bayes, Maximum Entropy, Long Short-Term Memory, Bi-Directional Long Short-Term Memory) on the Vietnamese Students' Feedback Corpus collected from a university. The final results were compared and evaluated to find the most effective model based on different evaluation criteria. The experimental results show that Bi-Directional Long Short-Term Memory algorithm outperformed than three other algorithms in term of the F1-score measurement with 92.0% on the sentiment classification task and 89.6% on the topic classification task. In addition, we developed a sentiment analysis application analyzing student feedback. The application will help the institution to recognize students' opinions about a problem and identify shortcomings that still exist. With the use of this application, the institution can propose an appropriate method to improve the quality of training activities in the future.• Bi-Directional Long Short-Term Memory (Bi-LSTM)

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Vietnamese transition-based dependency parsing with supertag features

Cited by 9 publications

References 14 publications

Syntax-aware Neural Semantic Role Labeling with Supertags

Syntax-aware Neural Semantic Role Labeling with Supertags

Emotion Recognition for Vietnamese Social Media Text

Deep Learning versus Traditional Classifiers on Vietnamese Students’ Feedback Corpus

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