Yesterday night I found a nice paper about neural network used for word sense disambugation. But I did not read it over. This morning, when I did today's work scheme, I decided to read the paper firstly.
The idea of this paper is very nice, I think!
Through the context vector, we could build a lot of input models and output models, then train the network to get the optimal structure. Later we could input the openning testing corpus, simulated to get the results.
The idea is nice.
Just now I searched "灰色" in Super Star. And I got a book with a string "grey" on the cover. At the first glance, I thought it was wrong, as I thought it should be said "gray". In order to make sure the answer, I found "gray" and "grey" in a dictionary. The answer was that they are same but "grey system" usually to translate for "灰色系统". Had found this true, I searched the "grey system" in Internet. Wa! The returned answers were more related to "灰色系统" than "gray system".
I got the conclusion that when you needed to translate a English word to Chinese or Chinese word to English, you'd better search more and more detailed, or ask for other person.
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