English translation Chinese word sense disambiguation method based on neural network
A neural network and word meaning disambiguation technology, applied in the field of machine translation, can solve problems such as unsatisfactory results and no obvious boundaries of attention, and achieve the effect of improving English reading efficiency
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Embodiment 1
[0015] The embodiment of the present invention adopts the open source neural network machine translation software OpenNMT software package (http: / / opennmt.net / ), and the 1 million Chinese-English corpus used for training comes from the open source Niutrans software package (http: / / www.niutrans.com ), the English-Chinese dictionary comes from the ECDict project (https: / / github.com / skywind3000 / ECDICT).
[0016] This embodiment mainly includes two parts ( figure 1 ): training translation models and decoding word sense disambiguation.
[0017] In the stage of training the translation model, it is divided into four steps.
[0018] The first step is to extract English words and their variants according to ECDict’s English-Chinese dictionary file, find out all the corresponding Chinese meanings, and generate an English-Chinese dictionary, one word per line, for example, the format of all meanings of the word work and its variants is as follows :
[0019] work|||work, work, labor, ...
Embodiment 2
[0030] The English-Chinese dictionary, corpus processing, and decoding process are the same as in Example 1. The training translation model is trained using a two-layer convolutional neural network (CNN) with 500 hidden units and a global attention mechanism. Both the source language and the target language use 100,000 words amount, each layer uses a 512-dimensional word vector space, and the generated translation model is about 900MB. Using this translation model for restricted decoding, the accuracy of word meaning in sentences is 79.4%.
Embodiment 3
[0032] The English-Chinese dictionary, corpus processing, and decoding process are the same as those in Example 1. The training translation model uses a 6-layer 512-hidden unit transformer (Transformer) and 8 multi-head self-attention mechanisms for training. Both the source language and the target language use 100,000 The amount of words, each layer uses a 512-dimensional word vector space, and the generated translation model is about 3000MB. Using this translation model for restricted decoding, the accuracy of word meaning in sentences is 83.2%.
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