Abstract: A method of enhancing the accuracy of a question-answer system. Missing information from a corpus of data is identified. The missing information is any information that improves a confidence for a candidate answer to a question. A follow-on inquiry is generated. The follow-on inquiry prompts for the missing information to be provided. The follow-on inquiry is output to an external source. A response to the follow-on inquiry is received from the external source. The response is added to the corpus of data.

Abstract: A system for providing community for customer questions receives a customer question. The customer question may be classified into a classification from a plurality of classifications categorizing whether a question is answerable, needs expert assistance, needs more information, or is not answerable. Based on the classification and one or more incentives, the question may be further routed to an appropriate community. The interactions with a customer in receiving and answering the customer question may be recorded.

Abstract: Providing answers to questions based on any corpus of data implements a method that generates a number of candidate passages from the corpus that answer an input query, and finds the correct resulting answer by collecting supporting evidence from the multiple passages. By analyzing all retrieved passages and that passage's metadata in parallel, an output plurality of data structures is generated including candidate answers based upon the analyzing. Then, supporting passage retrieval operations are performed upon the set of candidate answers, and for each candidate answer, the data corpus is traversed to find those passages having candidate answer in addition to query terms. All candidate answers are automatically scored by a plurality of scoring modules, each producing a module score. The modules scores are processed to determine one or more query answers; and, a query response is generated based on the one or more query answers.

Abstract: A system, method and computer program product for conducting questions and answers with deferred type evaluation based on any corpus of data. The method includes processing a query including waiting until a “Type” (i.e. a descriptor) is determined AND a candidate answer is provided. Then, a search is conducted to look (search) for evidence that the candidate answer has the required Lexical Answer Type (e.g., as determined by a matching function that can leverage a parser, a semantic interpreter and/or a simple pattern matcher). Prior to or during candidate answer evaluation, a process is provided for extracting and storing collections of entity-type pairs from semi-structured text documents. During QA processing and candidate answer scoring, a process is implemented to match the query LAT against the lexical type of each provided candidate answer and generate a score judging a degree of match.

Abstract: A computer-implemented system, method and program product generates answers to questions in an input query text string. The method includes determining, by a programmed processor unit, a lexical answer type (LAT) string associated with an input query; automatically obtaining a candidate answer string to the input query from a data corpus; mapping the query LAT string to a first type string in a structured resource; mapping the candidate answer string to a second type string in the structured resource; and determining if the first type string and the second type string are disjointed; and scoring the candidate answer string based on the determination of the types being disjointed wherein the structured resource includes a semantic database providing ontological content.

Abstract: A system, method and computer program product for automatically estimating the confidence of a detected LAT to provide a more accurate overall score for an obtained candidate answer. A confidence “score” or value of each detected LAT is obtained, and the system and method performs combining the confidence score with a degree of match between a LAT and an AnswerType of the candidate answer to provide improved overall score for the candidate answer.

Abstract: A system, method and computer program product for conducting questions and answers with deferred type evaluation based on any corpus of data. The method includes processing a query including waiting until a “Type” is determined AND a candidate answer is provided; the Type is not required as part of a predetermined ontology but is a lexical/grammatical item. Then, a search is conducted for evidence that the candidate answer has the required LAT (e.g., as determined by a matching function leveraging a parser, a semantic interpreter and/or a pattern matcher). Alternately, the LAT may be matched to a known Ontological Type and then a candidate answer searched in an knowledge-base or database determined by that type. Then, all the evidence from all the different ways to determine that the candidate answer has the expected lexical answer type (LAT) is combined and one or more answers provided.

Abstract: An evidence evaluation method and system based on question answering converts a report of analyzed information and/or a model of information into a collection of questions, determines answers for the collection of questions. A fact in the report is marked as being supported if one or more of the answers for the collection of questions support the fact. A fact in the report of analyzed facts is marked as being refuted if one or more of the answers for the collection of questions refute the fact. The method and system also may collect the answers as evidence and add the evidence to the model of information to create an updated model of information. The steps may be repeated using the updated report and updated model.

Abstract: A system, method and computer program product for providing answers to questions based on any corpus of data. The method facilitates generating a number of candidate passages from the corpus that answer an input query, and finds the correct resulting answer by collecting supporting evidence from the multiple passages. By analyzing all retrieved passages and that passage's metadata in parallel, there is generated an output plurality of data structures including candidate answers based upon the analyzing. Then, by each of a plurality of parallel operating modules, supporting passage retrieval operations are performed upon the set of candidate answers, and for each candidate answer, the data corpus is traversed to find those passages having candidate answer in addition to query terms. All candidate answers are automatically scored causing the supporting passages by a plurality of scoring modules, each producing a module score.

Abstract: A system for providing community for customer questions receives a customer question. The customer question may be classified into a classification from a plurality of classifications categorizing whether a question is answerable, needs expert assistance, needs more information, or is not answerable. Based on the classification and one or more incentives, the question may be further routed to an appropriate community. The interactions with a customer in receiving and answering the customer question may be recorded.

Abstract: A computer-implemented system, method and program product generates answers to questions in an input query text string. The method includes determining, by a programmed processor unit, a lexical answer type (LAT) string associated with an input query; automatically obtaining a candidate answer string to the input query from a data corpus; mapping the query LAT string to a first type string in a structured resource; mapping the candidate answer string to a second type string in the structured resource; and determining if the first type string and the second type string are disjointed; and scoring the candidate answer string based on the determination of the types being disjointed wherein the structured resource includes a semantic database providing ontological content.

Abstract: The present disclosure relates generally to question answering systems and methods and, particularly, to systems and methods for domain adaptation in question answering.

Abstract: A system, method and computer program product for automatically estimating the confidence of a detected LAT to provide a more accurate overall score for an obtained candidate answer. A confidence “score” or value of each detected LAT is obtained, and the system and method performs combining the confidence score with a degree of match between a LAT and an AnswerType of the candidate answer to provide improved overall score for the candidate answer.

Abstract: A system, method and computer program product for conducting questions and answers with deferred type evaluation based on any corpus of data. The method includes processing a query including waiting until a “Type” (i.e. a descriptor) is determined AND a candidate answer is provided. Then, a search is conducted to look (search) for evidence that the candidate answer has the required Lexical Answer Type (e.g., as determined by a matching function that can leverage a parser, a semantic interpreter and/or a simple pattern matcher). Prior to or during candidate answer evaluation, a process is provided for extracting and storing collections of entity-type pairs from semi-structured text documents. During QA processing and candidate answer scoring, a process is implemented to match the query LAT against the lexical type of each provided candidate answer and generate a score judging a degree of match.

Abstract: System, method and computer program product for 1) preparing queries for retrieving related content based on existing data content. For instance, titles of existing documents or entities extracted from documents can be used as queries. 2) Retrieving content from other repositories of unstructured, semi-structured, or structured data. For instance, web pages can be retrieved using existing search engines. 3) Extracting smaller units of text from the retrieved content. For instance, web pages can be split into coherent paragraphs of text. 4) Judging the quality of the smaller units of text and their relatedness to existing data. For instance, paragraphs can be scored using a statistical model based on lexico-syntactic features and topic models. 5) Synthesizing new sources from high-quality related text. For instance, paragraphs that score above a threshold can be concatenated into a new document.

Abstract: This invention includes a workforce management system having a system bus, at least one database in communication with the system bus that includes data representative of workforce employees, and social networking data associated with the employees. A matching functional unit includes a text mining function for mining contextual information from the at least one database to generate context labels for an employee, a clustering function for generating concept labels for an employee, and a matching function that sorts and matches employees by the labels in accordance with a matching criteria. A user interface provides user input to the support operation of the workforce management system.

Abstract: A system, method and/or computer program product for automatically generating questions and answers based on any corpus of data. The computer system, given a collection of textual documents, automatically generates collections of questions about the documents together with answers to those questions. In particular, such a process can be applied to the so called ‘open’ domain, where the type of the corpus is not given in advance, and neither is the ontology of the corpus. The system improves the exploring of large bodies of textual information. Applications implementing the system and method include new types of tutoring systems, educational question-answering games, national security and business analysis systems, etc.

Abstract: An evidence evaluation method and system based on question answering converts a report of analyzed information and/or a model of information into a collection of questions, determines answers for the collection of questions. A fact in the report is marked as being supported if one or more of the answers for the collection of questions support the fact. A fact in the report of analyzed facts is marked as being refuted if one or more of the answers for the collection of questions refute the fact. The method and system also may collect the answers as evidence and add the evidence to the model of information to create an updated model of information. The steps may be repeated using the updated report and updated model.

Abstract: The disclosed system and method apply stores of factual information to correct errors in digital text, for example, generated from OCR, speech and/or handwriting recognition devices, and other automatic recognition devices. A text produced by OCR, speech recognition, handwriting recognition, and others may be processed to extract discussed facts. Databases of facts are searched based on information in the text. After comparing facts asserted in the text with the factual data from the databases, suggested corrections of the text are produced.

Abstract: A system, method and computer program product for conducting questions and answers with deferred type evaluation based on any corpus of data. The method includes processing a query including waiting until a “Type” (i.e. a descriptor) is determined AND a candidate answer is provided; the Type is not required as part of a predetermined ontology but is only a lexical/grammatical item. Then, a search is conducted to look (search) for evidence that the candidate answer has the required LAT (e.g., as determined by a matching function that can leverage a parser, a semantic interpreter and/or a simple pattern matcher). In another embodiment, it may be attempted to match the LAT to a known Ontological Type and then look for a candidate answer up in an appropriate knowledge-base, database, and the like determined by that type. Then, all the evidence from all the different ways to determine that the candidate answer has the expected lexical answer type (LAT) is combined and one or more answers are delivered to a user.