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Span Labeling Approach for Vietnamese and Chinese Word Segmentation
Pacific Rim International Conference on Artificial Intelligence, 2021In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SPAN SEG. We compare the span labeling approach with the conditional random field by using encoders with the same architecture.
Duc-Vu Nguyen +3 more
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On the segmentation of Chinese incremental words.
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2021In the present article, we report two eye-tracking experiments on how Chinese readers segment incremental words while reading Chinese. Incremental words are multicharacter words containing a subset of characters that constitute another word (referred to as the embedded word).
Junyi Zhou, Xingshan Li
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Word Segmentation for Chinese Judicial Documents
2019Word segmentation is an integral step in many knowledge discovery applications. However, existing word segmentation methods have problems when applying to Chinese judicial documents: (1) existing methods rely on large-scale labeled data which is typically unavailable in judicial documents, and (2) judicial document has its own language features and ...
Linxia Yao +7 more
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Pattern Recognition Letters, 2020
Recent studies show that the joint Chinese word segmentation and POS tagging can enhance the mutual interaction and yield better performances for two tasks.
Ling Zhao, Ailian Zhang, Y. Liu, Hao Fei
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Recent studies show that the joint Chinese word segmentation and POS tagging can enhance the mutual interaction and yield better performances for two tasks.
Ling Zhao, Ailian Zhang, Y. Liu, Hao Fei
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Self-Supervised Chinese Word Segmentation
2001We propose a new unsupervised training method for acquiring probability models that accurately segment Chinese character sequences into words. By constructing a core lexicon to guide unsupervised word learning, self-supervised segmentation overcomes the local maxima problems that hamper standard EM training.
Fuchun Peng, Dale Schuurmans
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Chinese Word Segmentation with Character Abstraction
2013Chinese word segmentation is an important and necessary problem to analyze Chinese texts. In this paper, we focus on the primary challenges in Chinese word segmentation: low accuracy of out-of-vocabulary word. To resolve this difficult problems, we group the “similar” characters to generate more abstract representation.
Le Tian 0003 +2 more
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Study on the Influencing Factors of Chinese Word Segmentation
2012 International Conference on Asian Language Processing, 2012Out-of-vocabulary words (OOV) and ambiguity are two important issues for Chinese word segmentation (CWS). In previous studies, the measurement of OOV has been clearly stated, while the measurement of ambiguity requires further clarification. This paper puts forward the concept and calculation method of latent ambiguity (LA), analyzes the relation and ...
Chi Xiu, Rou Song
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A Study of Chinese Word Segmentation Based on the Characteristics of Chinese
2013This paper introduces the research on Chinese word segmentation (CWS). The word segmentation of Chinese expressions is difficult due to the fact that there is no word boundary in Chinese expressions and that there are some kinds of ambiguities that could result in different segmentations.
Aaron Li-Feng Han +5 more
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Bigram Chinese Word Segmentation by Viterbi Algorithm
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009Chinese word segmentation is an important foundation for Chinese information processing. This paper proposes a new Chinese word segmentation model based on Bayesian network. In this model, Character alignment Viterbi algorithm, which treats the preceding word of each Chinese character as its state, and the N-gram probability as its state transition ...
Dan Liu, Weiguo Fang, Hong Zhou, Yan Li
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Chinese Word Segmentation Based on Deep Learning
Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 2018Chinese word segmentation is a fundamental task in the field of Chinese Natural Language Processing. In this paper, we propose a series of neural network architectures by combining Long Short-Term Memory Neural Network (LSTM) with Conditional Random Field (CRF).
Mengge Wang +4 more
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