Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME). However, the conversion is seriously compromised by the ambiguities of Chinese characters corresponding to pinyin as well as the predefined fixed vocabularies. To alleviate such inconveniences, we propose a neural P2C conversion model augmented by an online updated vocabulary with a sampling mechanism to support open vocabulary learning during IME working. Our experiments show that the proposed method outperforms commercial IMEs and state-of-the-art traditional models on standard corpus and true inputting history dataset in terms of multiple metrics and thus the online updated vocabulary indeed helps our IME effectively follows user inputting behavior.
To effectively utilize words for IMEs, many natural language processing (NLP) techniques have been applied. @cite_22 introduced a joint maximum n-gram model with syllabification for grapheme-to-phoneme conversion. @cite_24 used a trigram language model and incorporated word segmentation to convert pinyin sequence to Chinese word sequences. @cite_26 proposed an iterative algorithm to discover unseen words in corpus for building Chinese language model. @cite_28 described a method enlarging the vocabulary which can capture the context information. However, all the above methods require an predefined fixed vocabulary.
@cite_24 @cite_28 @cite_26 @cite_22
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Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input. The noisy channel model approach is successfully applied to various natural language processing tasks. Currently the main research focus of this approach is adaptation methods, how to capture characteristics of words and expressions in a target domain given example sentences in that domain. As a solution we describe a method enlarging the vocabulary of a language model to an almost infinite size and capturing their context information. Especially the new method is suitable for languages in which words are not delimited by whitespace. We applied our method to a phoneme-to-text transcription task in Japanese and reduced about 10 of the errors in the results of an existing method. The lexicon quality affects the performance of Chinese language model directly.However,the lexicon compi- lation is separated from Chinese language modeling,resulting in two severe problems:firstly,the current language models can not achieve the optimal performance due to the limitation of lexicon scale;secondly,it is hard to apply the current language models to special areas due to the absence of lexicon.This paper aims to improve the performance of Chinese language model by constructing optimal lexicon.Meanwhile,it can self-adapt the area of training corpus automatically. Firstly,this paper combines the lexicon compilation with Chinese language modeling and proposes an iterative algorithm framework.Under this framework,it proposes the concept of character lexical significance(CLS)to describe Chinese lexical principle.Together with the statistical features,a multi-feature based algorithm is proposed for Chinese lexicon construction.Finally,it proposes two heuristic rules to adjust the parameters so as to self-adapt the area of training corpus.From the experimental results,it is found that the system can obtain the optimal Chinese lexicon as well as the high-performance Chinese language model.Moreover,the proposed techniques can self-adapt the area of training corpus successfully. A process for producing a strong and water-resistant bond between aluminum or an aluminum-based alloy and a polysulphide material by means of a primer which is applied to the met al, characterized in that the primer is a solution of a strongly basic alkali met al compound such as alkali met al hydroxides, phosphates and carbonates. The present invention also includes articles treated by the aforementioned process.