Sentiment Analysis for Chinese Dataset with Tsetlin Machine

Zhang, Xuanyu; Zhou, Hao; Yu, Ke; Zhang, Xuan; Wu, Xiaofei; Yazidi, Anis

doi:10.1109/istm54910.2022.00010

Cited by 3 publications

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“…This suggests that TM is a promising tool in NLPm due to its superior performance and excellent interpretability. (A preliminary and very abridged version of some of these results was presented in [8] at the First International Symposium on the Tsetlin (ISTM 2022), in June, 2022, in Grimstad, Norway. )…”

Section: Introductionmentioning

confidence: 99%

Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

Zhang

Zhou

et al. 2023

Algorithms

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In Natural Language Processing (NLP), deep-learning neural networks have superior performance but pose transparency and explainability barriers, due to their black box nature, and, thus, there is lack of trustworthiness. On the other hand, classical machine learning techniques are intuitive and easy to understand but often cannot perform satisfactorily. Fortunately, many research studies have recently indicated that the newly introduced model, Tsetlin Machine (TM), has reliable performance and, at the same time, enjoys human-level interpretability by nature, which is a promising approach to trade off effectiveness and interpretability. However, nearly all of the related works so far have concentrated on the English language, while research on other languages is relatively scarce. So, we propose a novel method, based on the TM model, in which the learning process is transparent and easily-understandable for Chinese NLP tasks. Our model can learn semantic information in the Chinese language by clauses. For evaluation, we conducted experiments in two domains, namely sentiment analysis and spam review detection. The experimental results showed thatm for both domains, our method could provide higher accuracy and a higher F1 score than complex, but non-transparent, deep-learning models, such as BERT and ERINE.

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Section: Introductionmentioning

confidence: 99%

Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

Zhang

Zhou

et al. 2023

Algorithms

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Punctuation and lexicon aid representation: A hybrid model for short text sentiment analysis on social media platform

Li,

Zou

2024

Journal of King Saud University - Computer and Information Scie

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WAD-X: Improving Zero-shot Cross-lingual Transfer via Adapter-based Word Alignment

Ahmat,

Yang,

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Multilingual pre-trained language models (mPLMs) have achieved remarkable performance on zero-shot cross-lingual transfer learning. However, most mPLMs implicitly encourage cross-lingual alignment in pre-training stage, making it hard to capture accurate word alignment across languages. In this paper, we propose Word-align ADapters for Cross-lingual transfer (WAD-X) to explicitly align word representations of mPLMs via language-specific subspace. Taking a mPLM as the backbone model, WAD-X constructs subspace for each source-target language pair via adapters. The adapters use statistical alignment as the prior knowledge to guide word-level aligning in the corresponding bilingual semantic subspace. We evaluate our model across a set of target languages on three zero-shot cross-lingual transfer tasks: part-of-speech tagging (POS), dependency parsing (DP), and sentiment analysis (SA). Experimental results demonstrate that our proposed model improves zero-shot cross-lingual transfer on three benchmarks, with improvements of 2.19, 2.50, and 1.61 points in POS, DP, and SA tasks over strong baselines.

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Sentiment Analysis for Chinese Dataset with Tsetlin Machine

Cited by 3 publications

References 0 publications

Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

Punctuation and lexicon aid representation: A hybrid model for short text sentiment analysis on social media platform

WAD-X: Improving Zero-shot Cross-lingual Transfer via Adapter-based Word Alignment

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