Chinese pinyin input methods are very important for Chinese language processing. Actually, users may make typos inevitably when they input pinyin. Moreover, pinyin typo correction has become an increasingly important task with the popularity of smartphones and the mobile Internet. How to exploit the knowledge of users typing behaviors and support the typo correction for acronym pinyin remains a challenging problem. To tackle these challenges, we propose KNPTC, a novel approach based on neural machine translation (NMT). In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features. In this approach, we first obtain the transition probabilities between adjacent letters based on large-scale real-life datasets. Then, we construct the "ground-truth" alignments of training sentence pairs by utilizing these probabilities. Furthermore, these alignments are integrated into NMT to capture sensible pinyin typo correction patterns. KNPTC is applied to correct typos in real-life datasets, which achieves 32.77 increment on average in accuracy rate of typo correction compared against the state-of-the-art system.
Chen and Lee first tried to resolve the problem of Chinese pinyin typo correction @cite_7 . They designed a statistical typing model to correct the pinyin typos and then used a language model to convert a pinyin sequence to a Chinese characters sequence. However, their method can only model a few rules and thus only a limited number of errors can be corrected. proposed an error-tolerant pinyin input method called CHIME using a noisy channel model and language-specific features @cite_13 . Their method assumed that the user inputted pinyin sequence had already been properly segmented by the user, which is inconsistent with the situation in real life. Our model discards this assumption to make it more practical. Jia and Zhao proposed a joint graph model to find the global optimum of pinyin to Chinese conversion and typo correction for the input method engine, and achieved better results than previous studies @cite_2 . In this paper, we employ this joint graph model as one of our baseline systems.
@cite_13 @cite_7 @cite_2
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Since Japanese and Chinese languages have too many characters to be input directly using a standard keyboard, input methods for these languages that enable users to input the characters are required. Recently, input methods based on statistical models have become popular because of their accuracy and ease of maintenance. Most of them adopt word-based models because they utilize word-segmented corpora to train the models. However, such word-based models suffer from unknown words because they cannot convert words correctly which are not in corpora. To handle this problem, we propose a character-based model that enables input methods to convert unknown words by exploiting character-aligned corpora automatically generated by a monotonic alignment tool. In addition to the character-based model, we propose an ensemble model of both character-based and word-based models to achieve higher accuracy. The ensemble model combines these two models by linear interpolation. All of these models are based on joint source channel model to utilize rich context through higher order joint n-gram. Experiments on Japanese and Chinese datasets showed that the character-based model performs reasonably and the ensemble model outperforms the word-based baseline model. As a future work, the effectiveness of incorporating large raw data should be investigated. It is very import for Chinese language processing with the aid of an efficient input method engine (IME), of which pinyinto-Chinese (PTC) conversion is the core part. Meanwhile, though typos are inevitable during user pinyin inputting, existing IMEs paid little attention to such big inconvenience. In this paper, motivated by a key equivalence of two decoding algorithms, we propose a joint graph model to globally optimize PTC and typo correction for IME. The evaluation results show that the proposed method outperforms both existing academic and commercial IMEs. Chinese Pinyin input methods are very important for Chinese language processing. In many cases, users may make typing errors. For example, a user wants to type in "shenme" (???, meaning "what" in English) but may type in "shenem" instead. Existing Pinyin input methods fail in converting such a Pinyin sequence with errors to the right Chinese words. To solve this problem, we developed an efficient error-tolerant Pinyin input method called "CHIME" that can handle typing errors. By incorporating state-of-the-art techniques and language-specific features, the method achieves a better performance than state-of-the-art input methods. It can efficiently find relevant words in milliseconds for an input Pinyin sequence. This paper describes our system designed for the NLPCC 2015 shared task on Chinese word segmentation WS and POS tagging for Weibo Text. We treat WS and POS tagging as two separate tasks and use a cascaded approach. Our major focus is how to effectively exploit multiple heterogeneous data to boost performance of statistical models. This work considers three sets of heterogeneous data, i.e., Weibo @math , 10K sentences, Penn Chinese Treebank 7.0 @math , 50K, and People's Daily @math , 280K. For WS, we adopt the recently proposed coupled sequence labeling to combine @math , @math , and @math , boosting F1 score from @math baseline model trained on only @math to @math @math . For POS tagging, we adopt an ensemble approach combining coupled sequence labeling and the guide-feature based method, since the three datasets have three different annotation standards. First, we convert @math into the annotation style of @math based on coupled sequence labeling, denoted by @math . Then, we merge CTB7 and @math to train a POS tagger, denoted by @math , which is further used to produce guide features on @math . Finally, the tagging F1 score is improved from 87.93 to 88.99 +1.06 .