International Business Machines Corporation

David Glickman

Walter S Rosenbaum

James T Repass

Danny B Convis

Evon C Greanias

Janet G Russell

A method and apparatus for vectorizing text words for compact storage and spelling verification in a mini-processor system without the use of complex mathematics functions. A binary storage table contains a plurality of addressable binary numbers. Each character in an input word is converted into a numerical weighting value. The numerical weighting values for the characters in a word are used to index into a magnitude weighting table. The selected magnitude weights are summed to produce a vector magnitude representation for the input word. The numerical weighting values are also used to cumulatively access the binary storage table. The values output from the binary storage table are modulo-2 added and accumulated to produce a vector angle representation for the input word. The calculated magnitude and angle values are used to compactly store a dictionary memory of correctly spelled words. Words subsequently input for spelling verification are similarly converted to vector magnitude and angle representations for comparison to the stored dictionary to determine if the input words are spelled correctly.

Comparing input words to a word dictionary for correct spelling

A system that intelligently abstracts and archives a document for storage and interprets a free form user retrieval query to recall the document from the storage file. The system includes a method for automatically selecting keywords from the document using a parts of a speech directory. A method is given for weighing the importance or centrality of each keyword with respect to the document of its origin. Using the same logic paths, a free form query that describes the document in the same manner that it would have to be described to a secretary to 'find' it in a filing cabinet, the system automatically determines the key matching terms and finds the archived document(s) with the greatest affinity.

Office correspondence storage and retrieval system

A system for reducing the computation required to match a misspelled word against various candidates from a dictionary to find one or more words that represent the best match to the misspelled word. The major facility offered is the ability to computationally discern the degree of apparent match that exists between words that do not perfectly match a given target word without requiring the computationally tedious procedure of character by character positional matching which necessitates shifting and realignment to accommodate for differences between the candidate and target words due to character differences or added and dropped syllables. The system includes a method for storing and retrieving words from the dictionary based on their likelihood of being the correct version of a misspelled word and then reviewing those words further to reduce the number of candidates that must then be examined in a high resolution positional match to find the candidate(s) which matches the misspelled word with the greatest character affinity. This technique reduces the number of candidates in contention so as to make a high resolution match computationally feasible on a real-time basis. The discriminant potential and the real-time computational burden associated with the technique are balanced in an optimal manner.

Instantaneous alpha content prescan method for automatic spelling error

A system for reducing storage requirements and accessing times in a text processing machine for automatic spelling verification and hyphenation functions. The system includes a method for storing a word list file and accessing the word list file such that legal prefixes and suffixes are truncated and only the unique root element, or 'stem', of a word is stored. A set of unique rules is provided for prefix/suffix removal during compilation of the word list file and subsequent accessing of the word list file. Spelling verification is accomplished by applying the rules to the words whose spelling is to be verified and application of the said rules provides, under most circumstances, a natural hyphenation break point at the prefix-stem and stem-suffix junctions.

Stem processing for data reduction in a dictionary storage file

A system for reducing the computation required to match a misspelled word against various candidates from a dictionary to find one or more words that represent the best match to the misspelled word. The major facility offered is the ability to computationally discern the degree of apparent match that exists between words that do not perfectly match a given target word without requiring the computationally tedious procedure of character by character positional matching which necessitates shifting and realignment to accommodate for differences between the candidate and target words due to character differences or added and dropped syllables. The system includes a method for storing and retrieving words from the dictionary based on their likelihood of being the correct version of a misspelled word and then reviewing those words further using the Prescan Alpha Content Match to reduce the number of candidates that must then be examined in a high resolution positional match to find the candidate(s) which matches the mis-spelled word with the greatest character affinity. The Prescan Alpha Content Match reduces the number of candidates in contention so as to make a high resolution match computationally feasible on a real-time basis.

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Frederick, MD, United States of America

David Glickman