Abstract: Determining correspondence, association, and/or correlation between nodes of electronic forms based on schemas of those forms is described. Importing, merging, and/or annotating data from one electronic form into another electronic form based on correspondence, association, and/or correlation between nodes is also described.

Abstract: Determining correspondence, association, and/or correlation between nodes of electronic forms based on schemas of those forms is described. Importing, merging, and/or annotating data from one electronic form into another electronic form based on correspondence, association, and/or correlation between nodes is also described.

Abstract: Detecting typographical errors in a Japanese sentence by using a bottom-up approach analysis. The bottom-up analysis employs probabilities, dictionaries and heuristics to words that are found in morpho-lexical information derived from the Japanese sentence. This bottom-up approach combines valid phrases analyses into well-formed combined phrases, i.e., phrase lists, to determine the existence of “holes”. Holes are characters contained in the input sentence but not in the well-formed phrase lists. Probabilities are used to determine which phrase list is most representative of the input sentence. The hole contained in the phrase list having the lowest cost (highest probability) is analyzed to determine if it is a typographical error. This analysis includes checking the hole to determine if it is an extended dictionary and whether it is a proper noun.

Abstract: A Consistency Checker provides an improved method of analyzing a Japanese text document to identify inconsistently spelled words. The Consistency Checker utilizes a Reading Pair Database (RPD) and a Compressed Lexicon Database (CLD) to determine the reading units within a word, to calculate a Reading Pair Identification Number (RID) for each reading unit, to calculate a Sense Identification Number (SID) for each word, and to calculate a Spelling Variant Identification Number (SVID) for each word. Spelling variants are generated by combining variations of individual RIDs in the RID array. A Registry is updated to maintain statistics on all of the words within the document. An error field within the Registry indicates that the document contains more than one spelling variant of a particular word. The client program can access the Registry to alert a user to inconsistencies discovered in the document.

Abstract: A word breaking facility operates to identify words within a Japanese text string. The word breaking facility performs morphological processing to identify postfix bound morphemes and prefix bound morphemes. The word breaking facility also performs opheme matching to identify likely stem characters. A scoring heuristic is applied to determine an optimal analysis that includes a postfix analysis, a stem analysis, and a prefix analysis. The morphological analyses are stored in an efficient compressed format to minimize the amount of memory they occupy and maximize the analysis speed. The morphological analyses of postfixes, stems, and prefixes is performed in a right-to-left fashion. The word breaking facility may be used in applications that demand identity of selection granularity, autosummarization applications, content indexing applications, and natural language processing applications.

Abstract: A word breaking facility operates to identify words within a Japanese text string. The word breaking facility performs morphological processing to identify postfix bound morphemes and prefix bound morphemes. The word breaking facility also performs opheme matching to identify likely stem characters. A scoring heuristic is applied to determine an optimal analysis that includes a postfix analysis, a stem analysis, and a prefix analysis. The morphological analyses are stored in an efficient compressed format to minimize the amount of memory they occupy and maximize the analysis speed. The morphological analyses of postfixes, stems, and prefixes is performed in a right-to-left fashion. The word breaking facility may be used in applications that demand identity of selection granularity, autosummarization applications, content indexing applications, and natural language processing applications.