Abstract: A method converts from compressed language to natural language. The method includes receiving an element string. The element string is in a compressed language format and is extracted from a document in a structured document language. The method includes tokenizing the element string to form multiple element tokens, generating a token set from the element tokens, and generating a name string from multiple token sets. The name string is in a natural language format.

Abstract: Systems and methods for lean parsing are disclosed. An example method is performed by one or more processors of a system and includes retrieving form data including first sentence segments and second sentence segments, determining a first predicate structure for each of the sentence segments based on a set of operators within the first set of sentence segments, identifying known tokens within the second set of sentence segments, each of the known tokens appearing on a list of predetermined tokens, identifying new tokens within the second set of sentence segments, each of the new tokens not on the list, mapping each known and new token to at least one operator, determining a second predicate structure for each sentence segment based on the mapping, and generating a predicate argument structure incorporating the first and second predicate structures, the predicate argument structure ready for mapping to at least one machine executable function.

Abstract: This disclosure describes converting computer-executable predicate-argument structures for a specific field to field-specific predicated-argument structures to improve execution. In some implementations, a method can be performed by one or more processors of a computing device, and can include receiving one or more predicate-argument structures (PASs) associated with taxation-specific text and converting the one or more PASs into one or more tax-specific predicate-argument structures (TPASs). Converting the one or more PASs to one or more TPASs may include one or more of: defining terms in a segment based on a definition of the term from a different segment or line description (including from a different document); reordering nodes, replacing nodes, or removing nodes of a segment (such as based on one or more single segment tree traversal rules); or combining multiple PASs for multiple segments of a single line description based on one or more multiple segment tree traversal rules.

Abstract: A method and system parses natural language in a unique way, determining important words pertaining to a text corpus of a particular genre, such as tax preparation. Sentences extracted from instructions or forms pertaining to tax preparation, for example are parsed to determine word groups forming various parts of speech, and then are processed to exclude words on an exclusion list and word groups that don't meet predetermined criteria. From the resulting data, synonyms are replaced with a common functional operator and the resulting sentence text is analyzed against predetermined patterns to determine one or more functions to be used in a document preparation system.

Abstract: This disclosure describes converting computer-executable predicate-argument structures for a specific field to field-specific predicated-argument structures to improve execution. In some implementations, a method can be performed by one or more processors of a computing device, and can include receiving one or more predicate-argument structures (PASs) associated with taxation-specific text and converting the one or more PASs into one or more tax-specific predicate-argument structures (TPASs). Converting the one or more PASs to one or more TPASs may include one or more of: defining terms in a segment based on a definition of the term from a different segment or line description (including from a different document); reordering nodes, replacing nodes, or removing nodes of a segment (such as based on one or more single segment tree traversal rules); or combining multiple PASs for multiple segments of a single line description based on one or more multiple segment tree traversal rules.

Abstract: This disclosure provides systems, methods and apparatuses for converting natural language text in financial documents to mathematical operations for a financial management program. In some implementations, a computing device may obtain a first segment of computer readable text associated with a text capture in a financial document, parse only a portion of the first segment to generate a parsed segment, and determine translation information associated with the parsed segment to translate the parsed segment. The computing device may also store the parsed segment and the translation information in a data structure in memory, translate the parsed segment in the data structure based on the translation information, and output one or more computer readable mathematical operations corresponding to the translated segment. In this manner, the computing device may output the one or more computer readable mathematical operations without parsing all of the segment.

Abstract: A method includes performing, at a device, an analysis of a domain-specific corpus to identify a base term and a modifier term. The modifier term modifies the base term in at least a portion of the domain-specific corpus. The method also includes accessing, by the device, a first entry in lexicon data. The first entry includes core data corresponding to domain-independent lexical information for the base term. The method further includes adding, based on the analysis, non-core data to the first entry. The non-core data corresponds to domain-specific lexical information for the base term. The non-core data identifies the modifier term as a domain-specific modifier of the base term.

Abstract: A computer program product for domain-specific data generation. The computer program product including a computer-readable storage medium having program instructions embodied therewith. The program instructions executable by a processor to cause the processor to perform operations including performing an analysis on a domain-specific corpus to identify a base term and a modifier term. The modifier term modifies the base term in at least a portion of the domain-specific corpus. The operations also include accessing a first entry in lexicon data. The first entry including core data corresponding to domain-independent lexical information for the base term. The operations further include adding, based on the analysis, non-core data to the first entry, the non-core data corresponding to domain-specific lexical information for the base term, wherein the non-core data identifies the modifier term as a domain-specific modifier of the base term.

Abstract: A method and system parses natural language in a unique way, determining important words pertaining to a text corpus of a particular genre, such as tax preparation. Sentences extracted from instructions or forms pertaining to tax preparation, for example are parsed to determine word groups forming various parts of speech, and then are processed to exclude words on an exclusion list and word groups that don't meet predetermined criteria. From the resulting data, synonyms are replaced with a common functional operator and the resulting sentence text is analyzed against predetermined patterns to determine one or more functions to be used in a document preparation system.

Abstract: A method and system parses natural language in a unique way, determining important words pertaining to a text corpus of a particular genre, such as tax preparation. Sentences extracted from instructions or forms pertaining to tax preparation, for example are parsed to determine word groups forming various parts of speech, and then are processed to exclude words on an exclusion list and word groups that don't meet predetermined criteria. From the resulting data, synonyms are replaced with a common functional operator and the resulting sentence text is analyzed against predetermined patterns to determine one or more functions to be used in a document preparation system.

Abstract: A method includes obtaining an input text, identifying a first term in the input text, and accessing lexicon data to identify a first entry corresponding to the first term. The first entry includes core data corresponding to domain-independent lexical information for the first term, and non-core data corresponding to domain-specific lexical information for the first term. The method also includes determining that the non-core data of the first entry identifies a second term in the input text as a modifier of the first term. The method further includes generating a partially parsed and bracketed version of the input text. The partially parsed and bracketed version indicates that the second term modifies the first term in the input text. The method also includes generating a parsed version of the input text based on the partially parsed and bracketed version of the input text.

Abstract: A computer program product including a computer-readable storage medium that has program instructions embodied therewith. The program instructions executable to cause the processor to perform operations including obtaining an input text at a text parser, identifying a first term in the input text, and accessing lexicon data to identify a first entry corresponding to the first term. The first entry includes non-core data that corresponds to domain-specific lexical information for the first term. The operations further include determining that the non-core data identifies a second term in the input text as a modifier of the first term. The operations also include generating a partially parsed and bracketed (PPB) version of the input text. The PPB version indicates that the second term modifies the first term in the input text. The operations further include generating a parsed version of the input text based on the PPB version of the input text.

Abstract: A method includes performing, at a device, an analysis of a domain-specific corpus to identify a base term and a modifier term. The modifier term modifies the base term in at least a portion of the domain-specific corpus. The method also includes accessing, by the device, a first entry in lexicon data. The first entry includes core data corresponding to domain-independent lexical information for the base term. The method further includes adding, based on the analysis, non-core data to the first entry. The non-core data corresponds to domain-specific lexical information for the base term. The non-core data identifies the modifier term as a domain-specific modifier of the base term.

Abstract: A method includes obtaining an input text, identifying a first term in the input text, and accessing lexicon data to identify a first entry corresponding to the first term. The first entry includes core data corresponding to domain-independent lexical information for the first term, and non-core data corresponding to domain-specific lexical information for the first term. The method also includes determining that the non-core data of the first entry identifies a second term in the input text as a modifier of the first term. The method further includes generating a partially parsed and bracketed version of the input text. The partially parsed and bracketed version indicates that the second term modifies the first term in the input text. The method also includes generating a parsed version of the input text based on the partially parsed and bracketed version of the input text.

Abstract: A computer program product for domain-specific data generation. The computer program product including a computer-readable storage medium having program instructions embodied therewith. The program instructions executable by a processor to cause the processor to perform operations including performing an analysis on a domain-specific corpus to identify a base term and a modifier term. The modifier term modifies the base term in at least a portion of the domain-specific corpus. The operations also include accessing a first entry in lexicon data. The first entry including core data corresponding to domain-independent lexical information for the base term. The operations further include adding, based on the analysis, non-core data to the first entry, the non-core data corresponding to domain-specific lexical information for the base term, wherein the non-core data identifies the modifier term as a domain-specific modifier of the base term.

Abstract: A method and system parses natural language in a unique way, determining important words pertaining to a text corpus of a particular genre, such as tax preparation. Sentences extracted from instructions or forms pertaining to tax preparation, for example are parsed to determine word groups forming various parts of speech, and then are processed to exclude words on an exclusion list and word groups that don't meet predetermined criteria. From the resulting data, synonyms are replaced with a common functional operator and the resulting sentence text is analyzed against predetermined patterns to determine one or more functions to be used in a document preparation system.

Abstract: According to an aspect, a candidate token sequence including one or more word tokens is extracted from an unstructured domain glossary that includes entries associated with a domain. A look-up operation is performed to retrieve language data for each word token in the candidate token sequence and annotates each word token in the candidate token sequence found by the look-up operation with corresponding retrieved language data to form an annotated sequence. A pattern match of the annotated sequence is performed relative to a repository of patterns and identifies a best matching pattern from the repository of patterns to the annotated sequence based on matching criteria. The annotated sequence is refined with lexical information associated with the best matching pattern as a refined annotated sequence. The candidate token sequence and the refined annotated sequence are output to a domain-specific computational lexicon file.

Abstract: According to an aspect, a candidate token sequence including one or more word tokens is extracted from an unstructured domain glossary that includes entries associated with a domain. A look-up operation is performed to retrieve language data for each word token in the candidate token sequence and annotates each word token in the candidate token sequence found by the look-up operation with corresponding retrieved language data to form an annotated sequence. A pattern match of the annotated sequence is performed relative to a repository of patterns and identifies a best matching pattern from the repository of patterns to the annotated sequence based on matching criteria. The annotated sequence is refined with lexical information associated with the best matching pattern as a refined annotated sequence. The candidate token sequence and the refined annotated sequence are output to a domain-specific computational lexicon file.

Abstract: According to an aspect, a candidate lexical kernel unit that includes a word token sequence having two or more words is received. Domain terms that contain the two or more words are retrieved from a terminology resource file of domain terms associated with a domain. The candidate lexical kernel unit and the retrieved domain terms are analyzed to determine whether the candidate lexical kernel unit satisfies specified criteria for use as a building block by a natural-language processing (NLP) tool for building larger lexical units in the domain. Each of the larger lexical units includes a greater number of words than the candidate lexical kernel unit. The candidate lexical kernel unit is identified as a lexical kernel unit based on determining that the candidate lexical kernel unit satisfies the specified criteria. The lexical kernel unit is output to a domain-specific lexical kernel unit file for input to the NLP tool.

Abstract: According to an aspect, a candidate lexical kernel unit that includes a word token sequence having two or more words is received. Domain terms that contain the two or more words are retrieved from a terminology resource file of domain terms associated with a domain. The candidate lexical kernel unit and the retrieved domain terms are analyzed to determine whether the candidate lexical kernel unit satisfies specified criteria for use as a building block by a natural-language processing (NLP) tool for building larger lexical units in the domain. Each of the larger lexical units includes a greater number of words than the candidate lexical kernel unit. The candidate lexical kernel unit is identified as a lexical kernel unit based on determining that the candidate lexical kernel unit satisfies the specified criteria. The lexical kernel unit is output to a domain-specific lexical kernel unit file for input to the NLP tool.