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Symbolic approach: encode all required information into computer(rationalism)
linguistic knowledge(static knowledge, context-dependent knowledge)
world knowledge(uniqueness of reference, type of num, situational associativity between noun)
将所有需要的信息编码到计算机中(理性主义)
语言知识(静态知识、语境相关知识)
世界知识(引用的唯一性,num的类型,名词之间的情景联想性)
Statistic approach: infer language properties from language samples(empiricism)
Collect a large collection of texts relevant to your domain
For each noun, compute its probability to take a certain determiner
P(determiner | noun)= n o u n , d e t e r m i n e r f r e q ( n o u n ) \frac{noun,determiner}{freq(noun)} freq(noun)noun,determiner
Given a new noun, select a determiner with the highest likelihood as estimated on the training corpus
从语言样本中推断语言特性(经验主义)
收集大量与您的领域相关的文本
对于每个名词,计算它取某个限定词的概率
Big5: the first byte ranges from 0xA0-0xF9Big5: the first byte ranges from 0xA0-0xF9
the second byte ranges from 0x40-0x7e, 0xA0 to 0xFE, ASCII characters are still represented with a single byte
one sense per collocation,
one sense per discourse
https://baike.baidu.com/item/隐马尔可夫模型/7932524?fr=aladdin
https://www.cnblogs.com/skyme/p/4651331.html
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