[특허]Building scalable N-gram language models using maximum likelihood maximum entropy N-gram models

Building scalable N-gram language models using maximum likelihood maximum entropy N-gram models 원문보기

IPC분류정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G10L-009/00
출원번호	US-0023543 (1993-02-26)
발명자 / 주소	Lau Raymond (Cambridge MA) Rosenfeld Ronald (Pittsburgh PA) Roukos Salim (Scarsdale NY)
출원인 / 주소	International Business Machines Corporation (Armonk NY 02)
인용정보	피인용 횟수 : 104 인용 특허 : 0

초록 ▼

The present invention is an n-gram language modeler which significantly reduces the memory storage requirement and convergence time for language modelling systems and methods. The present invention aligns each n-gram with one of “n”number of non-intersecting classes. A count is determined for each n-gram representing the number of times each n-gram occurred in the training data. The n-grams are separated into classes and complement counts are determined. Using these counts and complement counts factors are determined, one factor for each class, using an iterative scaling algorithm. The language model probability, i.e., the probability that a word occurs given the occurrence of the previous two words, is determined using these factors.

대표청구항 ▼

A computer based language modelling system receiving data in the form of training text divided into a series of n-grams, each n-gram comprising a series of “n”words, each n-gram having an associated count, the history of an n-gram being represented by the initial n-1 words of the n-gram, comprising: a language modelling means for determining a conditional probability of a predicted word given the previous (n-1) words, comprising: a memory means for storing the data; a separating means coupled to said memory means for examining each word within each n-gram and classifying each n-gram into one of one or more classes based upon one or more words in a given n-gram, each class having one or more similar n-grams associated with said class, said similar n-grams having the same predicted word and x previous words, where x varies from (n-1) to zero, to associate each n-gram with exactly one of said one or more classes, each class is identified with one of one or more sets based upon the value of x used when determining the class of the n-gram; a factor means coupled to the output of said separating means and to said memory means for determining a factor for each of said one or more classes, said factor representing the relative strength of predicting said predicted word given the previous (n-1) words, the value of each factor being approximately equal to the ratio of the sum of the counts of each n-gram associated with a given class over the sum of the counts of all (n-1)-grams which when followed by said predicted word would belong to said given class; and a conditional probability means coupled to the output of said factor means for determining said conditional probability of the occurrence of said predicted word given that a particular sequence of (n-1) previous words have occurred using said factors, said conditional probability approximately equal to the ratio of a first factor, said first factor associated with the class that a given n-gram is associated with, said given n-gram equal to said predicted word and the history of said predicted word, the history equal to a particular sequence of (n-1) previous words, over the sum of one or more factors, said one or more factors associated with all of the classes of n-grams obtained by using said particular sequence of (n-1) words followed by any word of the vocabulary.

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Building scalable N-gram language models using maximum likelihood maximum entropy N-gram models 원문보기

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Building scalable N-gram language models using maximum likelihood maximum entropy N-gram models 원문보기

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