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Neural Word Segmentation Learning for Chinese [PDF]
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016 Most previous approaches to Chinese word segmentation formalize this problem as a character-based sequence labeling task where only contextual information within fixed sized local windows and simple interactions between adjacent tags can be captured. In this paper, we propose a novel neural framework which thoroughly eliminates context windows and can ...
Deng Cai 0002, Hai Zhao 0001
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Multi-Grained Chinese Word Segmentation [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence. However, Sproat et al. (1997) show that the inter-native-speaker consistency ratio over Chinese word boundaries is only 76%, indicating single-grained WS (SWS) imposes unnecessary challenges on both manual annotation and ...
Chen Gong 0004 +3 more
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Bootstrapping word alignment via word packing [PDF]
, 2007 We introduce a simple method to pack words for statistical word alignment. Our goal is to simplify the task of automatic word alignment by packing several consecutive words together when we believe they correspond to a single word in the opposite ...
Stroppa, Nicolas, Way, Andy, Ma, Yanjun
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A benchmark dataset and case study for Chinese medical question intent classification
BMC Medical Informatics and Decision Making, 2020 Background To provide satisfying answers, medical QA system has to understand the intentions of the users’ questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learning approach in a supervised way ...
Nan Chen +4 more
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The role of format familiarity and word frequency in Chinese reading
Journal of Eye Movement Research, 2023 For Chinese readers, reading from left to right is the norm, while reading from right to left is unfamiliar. This study comprises two experiments investigating how format familiarity and word frequency affect reading by Chinese people.
Mingjing Chen, Jiamei Lu
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Analysing the Methods of Dzongkha Word Segmentation
Applied Computer Systems, 2017 In both Chinese and Dzongkha languages, the greatest challenge is to identify the word boundaries because there are no word delimiters as it is in English and other Western languages.
Dhungyel Parshu Ram +1 more
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The Trade-Off Between Format Familiarity and Word-Segmentation Facilitation in Chinese Reading
Frontiers in Psychology, 2021 In alphabetic writing systems (such as English), the spaces between words mark the word boundaries, and the basic unit of reading is distinguished during visual-level processing.
Mingjing Chen +4 more
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Synthetic Word Parsing Improves Chinese Word Segmentation [PDF]
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2015 We present a novel solution to improve the performance of Chinese word segmentation (CWS) using a synthetic word parser. The parser analyses the internal structure of words, and attempts to convert out-of-vocabulary words (OOVs) into in-vocabulary fine-grained sub-words.
Fei Cheng 0002 +2 more
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Introduction to CKIP Chinese word segmentation system for the first international Chinese Word Segmentation Bakeoff [PDF]
Proceedings of the second SIGHAN workshop on Chinese language processing -, 2003 In this paper, we roughly described the procedures of our segmentation system, including the methods for resolving segmentation ambiguities and identifying unknown words. The CKIP group of Academia Sinica participated in testing on open and closed tracks of Beijing University (PK) and Hong Kong Cityu (HK).
Wei-Yun Ma, Keh-Jiann Chen
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Word-Context Character Embeddings for Chinese Word Segmentation [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented ...
Hao Zhou 0012 +5 more
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