Chinese transliteration manual#

Then we obtain probable transliterations of the given English word. In the second step, we apply phoneme chunks, extracted from training data, to calculate the reliability of each possible transliteration. In the first, we make a phoneme network that shows all possible transliterations of the given word. The E-K transliteration method has three steps. By applying phoneme chunks, we combine different length information with easy.

Chinese transliteration manual#

Therefore an automatic transliteration system is needed to find the transliterations of English words without manual intervention.To explain E-K transliteration phenomena, we use phoneme chunks that do not have a length limit.

As English words and Korean transliterations are usually technical terms and proper nouns, it is hard to find a transliteration and its variations in a dictionary. In Korean technical documents, many English words are transliterated into Korean words in various forms in diverse ways. We present in this paper the method of English-to-Korean (E-K) transliteration and back-transliteration. A phrase-based statistical machine translation framework for joint decoding and a conditional random field syllabification system are used in this work for our investigation, which shows a different scenario on the issue of joint decoding versus cascade processing in transliteration. Thus in this paper, we investigate the two routines with a systematic analysis and compare their results to illustrate the strength of syllabification. Usually, joint decoding outperforms the cascade processing in many natural language processing missions, however, syllabification is a special component in transliteration task. In grapheme-based approaches, there are two routines to transliterate, one is to perform in a pipeline of separate syllabification and other components in generation process step by step, the other is to synchronously segment syllables and generating transliteration options. Since the conversion relates to the phonetic aspect of a text, syllabification is considered a major factor affecting the performance of a transliteration system. Transliteration is a challengeable task aimed at converting a proper name into another language with phonetic equivalence. © 2014 Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. This significantly reduces overfitting and gives major improvements over other regularization methods. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. During training, dropout samples from an exponential number of different 'thinned' networks. This prevents units from co-adapting too much.

The key idea is to randomly drop units (along with their connections) from the neural network during training. Dropout is a technique for addressing this problem. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. However, overfitting is a serious problem in such networks. ĭeep neural nets with a large number of parameters are very powerful machine learning systems. The Acquisition of Chinese Characters: Corpus Analyses and Connectionist Simulations. We further modeled character acquisition in a self-organizing connectionist network, developing orthographic representations onthe basis of our analysis of character properties in a large-scale character corpus.Our model is able to faithfully capture the orthographic similarities of Chinesecharacters, and moreover, display effects of regularity, consistency, characterfrequency, and the complex interactions among them, matching up well withavailable empirical evidence on children’s acquisition of characters during theelementary school years. In this study, we set out to analyze a realistic character corpus aslearned by children in Chinese elementary school, in order to evaluate thedegree to which corpus analysis can inform us of the roles that regularity,consistency, frequency, and their interactions play in children’s acquisition ofChinese characters. However, despite growing interests in the Chineseorthography, few previous studies have systematically analyzed these effects ona large scale, or have modeled the acquisition process using connectionistnetworks. Empirical evidence has accumulated that regularity and consistency inorthography-to-phonology correspondence both affect the processing andacquisition of Chinese characters, and that they interact with character frequencyin complex ways.