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  • Lichi Yuan
  • 2010
In this paper, the Markov Family Models, a kind of statistical Models was firstly introduced. Under the assumption that the probability of a word depends both on its own tag and previous word, but its own tag and previous word are independent if the word is known, we simplify the Markov Family Model and use for part-of-speech tagging successfully.(More)
  • Lichi Yuan
  • 2008
In order to overcome the defects of the duration modeling of homogeneous HMM in speech recognition and the unrealistic assumption that successive observations are independent and identically distribution within a state, Markov family model (MFM), a new statistical model is proposed in this paper. Independence assumption is placed by conditional independence(More)
Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and not large amount of computation. (2) class-based method always loses some prediction ability to(More)
  • Lichi Yuan
  • 2015
Category-based statistical language model is an important method to solve the problem of sparse data, but there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and has not large amount of computation. (2) class-based method always loses some prediction(More)
  • Lichi Yuan
  • 2006
In order to overcome the defects of the duration modeling of homogeneous HMM in speech recognition and the unrealistic assumption that successive observations are independent and identically distribution within a state, Markov family model (MFM) is proposed in this paper. Independence assumption is placed by conditional independence assumption in Markov(More)
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