Named Entity Recognition in Vietnamese Tweets

Nguyen, Vu; Nguyen, Hien; Snášel, Václav

doi:10.1007/978-3-319-21786-4_18

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The named-entity detector applies the rules to the text and assigns the correct label (tag) for each word. The authors in [2] built a Malayalam NER classifier and in [17] for Vietnamese by using a neural network. Liu and et al in [14] provided a semisupervised learning framework to recognize entities in English tweets by combining a linear conditional random fields (CRF) model and K-nearest neighbors (KNN) classifier.…”

Section: A Name Entity Recognition Systemsmentioning

confidence: 99%

Visualization of Arabic Entities in Online Social Media using Machine Learning

Alyamani¹,

Saudagar²

2021

IJACSA

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In recent years, the use of social media and the amount of exchangeable data have increased considerably. The increase in exchangeable data makes data mining, analysis and visualization of relevant information a challenging task. This research work assesses, categorizes, and analyzes Arabic entities on social media selected by users at certain time intervals. To accomplish this aim, the authors built a highly efficient classification model to classify entities according to three categories: person, location, and organization. The developed model captures an entity and specific time, collects all the posts on tweeter that refer to the entity at this specific time, and then classifies, visualize the entity through three methods. It first starts with classifying the entity through a corpus model that depends on customized corpus. If the entity is not classified through that model, it will be send to an indicators model which uses the pre-indicators or post-indicators for classing. Finally, the entity is passed to a gazetteer model which searches for the entity in three gazetteers (person, location, and organization), and accordingly determines the number of times the entity reference is repeated. This work allows scholars and researchers in different fields to visualize the frequency of entities referenced by a community. It also compares how references to entities change over time. The experimental results show that accuracy of the developed model in classifying the tweets is nearly 90%.

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Section: A Name Entity Recognition Systemsmentioning

confidence: 99%

Visualization of Arabic Entities in Online Social Media using Machine Learning

Alyamani¹,

Saudagar²

2021

IJACSA

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Vietnamese Spelling Error Detection and Correction Using BERT and N-gram Language Model

Tien¹,

Minh²,

Vu³

et al. 2022

Intelligent Systems and Networks

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An empirical study of discriminative sequence labeling models for Vietnamese text processing

Le-Hong

Nhat

Pham³

et al. 2017

2017 9th International Conference on Knowledge and Systems Engineering (KSE)

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Abstract-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 handcrafted 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.

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Named Entity Recognition in Vietnamese Tweets

Cited by 5 publications

References 19 publications

Visualization of Arabic Entities in Online Social Media using Machine Learning

Visualization of Arabic Entities in Online Social Media using Machine Learning

Vietnamese Spelling Error Detection and Correction Using BERT and N-gram Language Model

An empirical study of discriminative sequence labeling models for Vietnamese text processing

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