Method for part-of-speech determination and usage

Claim 2

An automated method for assigning parts of speech to words in a message, of the type comprising the steps of: electronically reading stored representations of the message, generating lexical probabilities for each word to be a particular part of speech, and - selecting, in response to the lexical probability for the subject word and in response to the contextual probabilities for at least one adjacent word to be a particular part of speech, the contextual probability for the subject word to be a particular part of speech, SAID METHOD BEING CHARACTERIZED IN THAT: the generating step includes representing certain words, spaces before and after sentences and punctuation symbols as words having empirically-determined frequencies of occurrence in a non-verbal record of the message including, smoothing part of speech frequencies for at least certain word; and the selecting step includes maximizing the contextual probabilities referred to parts of speech of nearby words including at least the following word.

Claim 3

An automated method of the type claimed in claim 1, _ FURTHER CHARACTERIZED BY the steps: , assigning all possible noun phrase boundaries, eliminating all non-paired boundaries, and optimizing contextual noun phrase boundary probabilities.

Claim 4

An automated method of the type claimed in claim 2, FURTHER CHARACTERIZED BY the step: assigning word stress dependent upon the results of the optimizing steps.

Claim 5

An automated method of the type claimed in claim 3, FURTHER CHARACTERIZED BY employing means responsive to the assigned word stress for synthesizing speech corresponding to the message.

Claim 6

An automated method of the type claimed in claim 2, FURTHER CHARACTERIZED BY employing the highest selected contextual probabilities for the words in a message to detect contextual errors in the message.

Claim 7

An automated method for determining, in a message, beginnings and ends of noun phrases to which parts of speech have been assigned with reasonable probability, of the type including the steps of estimating whether the words around each noun in the message could be part of a noun phrase, and utilizing the resulting estimates, SAID METHOD BEING CHARACTERIZED BY the steps of: assigning all possible noun phrase boundaries, eliminating all non-paired boundaries, and optimizing contextual noun phrase boundary probabilities.

Claim 8

An automated method of the type claimed in claim 1 or 6, SAID METHOD BEING CHARACTERIZED BY assigning parts of speech in the message by n-gram analysis with respect to the parts of speech of near-by words, including the steps of representing certain non-words as words having empirically determined frequencies of occurrence in a non-verbal record of the message, computing an optimum normalized contextual probability for each other near-by word in the message to be a particular part of speech in relationship to the contextual part-of-speech probabilities of differing uses of said non-words, where the normalized contextual probability is the trigram part-of-speech probability divided by the bigram part-of-speech probability, all determined by starting at the end of the sentence, including blank spaces.

Claim 9

An automated method of the type claimed in claim 1 SAID METHOD BEING FURTHER CHARACTERIZED IN THAT the generating step includes smoothing frequencies by reference to a dictionary for parts-of-speech usage of words having relatively low frequencies of occurrence as a particular part of speech, and the selecting step further includes determining the product of lexical probability of the contextual probability, where the lexical probability is estimated as the quotient of the frequency of occurrence of the word as a particular part of speech, divided by its frequency of occurrence as all parts of speech, and the contextual probability is estimated by dividing the trigram frequency by the bigram frequency, where the trigram frequency is the frequency of occurrence of the particular part of speech in sequence with the two following parts of speech, as already determined for the two following words, and the bigram frequency is the frequency of occurrence of the particular part of speech of the following word in sequence with the next-following part of speech, as already determined for the next-following word; and reiterating the determining step for a number of possible part-of-speech combinations, including retaining products which exceed prior products for the same word.