An empirical study on POS tagging for Vietnamese social media text

Bach, Ngo Xuan; Linh, Nguyen Dieu; Phương, Từ Minh

doi:10.1016/j.csl.2017.12.004

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…Regarding to the Vietnamese language processing, there are quite a number of research works on other tasks such as parsing [50]- [52], part-of-speech [53], [54], named entity recognition [55]- [57], sentiment analysis [58]- [60], and question answering [61]- [63]. However, to the extent of our knowledge, there are no research publications on multiplechoice machine reading comprehension.…”

Section: B Mrc Methodsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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Although Vietnamese is the 17 th most popular native-speaker language a in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexicalbased MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website b for research purposes.

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Section: B Mrc Methodsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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“…Finally, it should be noted that, at the time of writing this paper, Modern Greek data sets and text mining approaches are certainly fewer in number when compared against works focusing on other, more widely spoken, popular languages, such as English, German, Spanish, French, Italian, Russian or Chinese. In general, when compared to corresponding tasks in other languages, the obtained accuracy results in Modern Greek web text mining tasks indicate a better overall performance than [50][51][52][53][54][55][56][57], comparable to [58][59][60], or worse than [61][62][63].…”

Section: Approaches On Developing Modern Greek Social Web Text Data Sets and On Modernmentioning

confidence: 98%

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Nikiforos

Voutos

Drougani

et al. 2021

Data

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Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

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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%

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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An empirical study on POS tagging for Vietnamese social media text

Cited by 19 publications

References 8 publications

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Emotion Recognition for Vietnamese Social Media Text

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