Bag of biterms modeling for short texts

Tuan, Anh; Tran, Bach Xuan; Nguyen, Thien Huu; Linh, Ngo Van; Than, Khoat

doi:10.1007/s10115-020-01482-z

Cited by 9 publications

(2 citation statements)

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“…Simultaneously, it should be plastic when concept drift happens. Secondly, noisy and sparse data (Nguyen et al, 2021;Ha et al, 2019;Mai et al, 2016;Tuan et al, 2020) makes a lot of difficulties for learning methods. While sparse data does not provide an unclear context, noisy data can mislead the methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive infinite dropout for noisy and sparse data streams

et al. 2022

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The ability to analyze data streams, which arrive sequentially and possibly infinitely, is increasingly vital in various online applications. However, data streams pose various challenges, including sparse and noisy data as well as concept drifts, which easily mislead a learning method. This paper proposes a simple yet robust framework, called Adaptive Infinite Dropout (aiDropout), to effectively tackle these problems. Our framework uses a dropout technique in a recursive Bayesian approach in order to create a flexible mechanism for balancing between old and new information. In detail, the recursive Bayesian approach imposes a constraint on the model parameters to make a regularization term between the current and previous mini-batches. Then, dropout whose drop rate is autonomously learned can adjust the constraint to new data. Thanks to the ability to reduce overfitting and the ensemble property of Dropout, our framework obtains better generalization, thus it effectively handles undesirable effects of noise and sparsity. In particular, theoretical analyses show that aiDropout imposes a data-dependent regularization, therefore, it can adapt quickly to sudden changes from data streams. Extensive experiments show that aiDropout significantly outperforms the state-of-the-art baselines on a variety of tasks such as supervised and unsupervised learning.

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Section: Introductionmentioning

confidence: 99%

Adaptive infinite dropout for noisy and sparse data streams

et al. 2022

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“…If bi-grams were instead extracted, only adjacent relationships will be identified. However, the use of bigrams has been shown to only be suitable for long texts but ineffective for short texts such as questions since the frequency of bi-grams is low; this leads to inefficient modeling of word co-occurrence and dependency for subsequent classification [183].…”

Section: Qualitative Analysismentioning

confidence: 99%

Domain-agnostic document and question classification using natural language processing techniques

Supraja¹

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I would like to dedicate this thesis to two special angels: God and my dear parents, without whom I could not have completed this challenging, yet fruitful PhD journey. I have no words to express my gratitude for every single thing my parents have done for my education and for my whole life. I cannot do anything to repay their love, affection, care, and concern for me.I would like to extend my sincere thanks to my husband, all my family members and friends who have supported me in one way or another. First and foremost, I would like to sincerely express my gratitude to my advisor A/Prof Andy W. H. Khong for his continuous support and guidance throughout my Ph.D. journey. His constant motivation and encouragement has transformed me tremendously.His advice has always been a great tonic to boost my morale and confidence level.

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ETBTRank: Ranking Biterms in Paper Titles for Emerging Topic Discovery

Huang

Zarei

2022

Lecture Notes in Computer Science

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Bag of biterms modeling for short texts

Cited by 9 publications

References 20 publications

Adaptive infinite dropout for noisy and sparse data streams

Adaptive infinite dropout for noisy and sparse data streams

Domain-agnostic document and question classification using natural language processing techniques

ETBTRank: Ranking Biterms in Paper Titles for Emerging Topic Discovery

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