文件名称:Class-based_n-gram_models_of_natural_language
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We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.-We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithmsfor assigning words to classes based on the frequency of their co- occurrence with other words. Wefind that we are able to extract classes that have the flavor of either syntactically based groupingsor semantically based groupings, depending on the nature of the underlying statistics.
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.-We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithmsfor assigning words to classes based on the frequency of their co- occurrence with other words. Wefind that we are able to extract classes that have the flavor of either syntactically based groupingsor semantically based groupings, depending on the nature of the underlying statistics.
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Class-based n-gram models of natural language.pdf