Abstract: A system, and computer program product for disambiguation of dependent referring expression in natural language processing are provided in the illustrative embodiments. A portion of a document in a set of document is selected, the portion including a set of dependent referring expression instances. The portion is filtered to identify an instance from a set of dependent referring expression instances by using a linguistic characteristic of the instance, the instance of dependent referring expression referring to a full expression occurring in the set of documents. The full expression is located in one member document in the set of documents by locating where the dependent referring expression is defined to be a stand-in for the full expression. The instance is resolved using the full expression such that information about the full expression is available at a location of the instance.

Abstract: Mechanisms are provided in a question answering (QA) system comprising a QA system pipeline that analyzes an input question and generates an answer to the input question, for pre-processing the input question. The mechanisms receive an input question and input the input question to a pre-processor flow path having one or more pre-processors. The one or more pre-processors transform the input question into a transformed question by correcting errors in a formulation of the input question that are determined to be detrimental to efficient and accurate processing of the input question by a QA system pipeline of the QA system. The transformed question is then input to the QA system pipeline of the QA system which processes the transformed question to generate and output an answer to the input question.

Abstract: A mechanism is provided in a computing system for interpreting and distinguishing a lack of an answer to an input question. The mechanism receives an input question from a user. The mechanism generates one or more hypotheses based on a corpus of source information. The mechanism injects a no-answer response into the one or more hypotheses to form a plurality of candidate answers. The mechanism ranks the plurality of candidate answers based on evidence scoring and confidence scoring to form a candidate answer ranking. Responsive to determining the no-answer response is the top candidate answer in the candidate answer ranking, the mechanism presents the no-answer response to the user.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for utilizing temporal indicators to weight semantic values. A set of temporal characteristics is identified of a set of initial candidate answers. For each initial candidate answer in the set of initial candidate answers: a distance value is generated for each of the set of temporal characteristics of the set of initial candidate answers, a multiplier value is determined with which to weight an initial confidence score associated with the initial candidate answer using the distance value; a sentiment value is determined of the initial candidate answer, and a final weight value is determined using the multiplier value, the sentiment value, and the initial confidence score associated with the initial candidate answer. A set of temporally refined candidate answers is then provided using the determined final weight values.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a natural language processing (NLP) system, for identifying content relationship for content copied by a content identification mechanism. The content identification mechanism identifies content from a website and then identifies relationship content information associated with a current web page where the content is found. The content identification mechanism modifies a file structure associated with the content with the relationship content information. The content identification mechanism identifies one or more classification identifiers in order to classify the content. Finally, the content identification mechanism transmits the content and the file structure to a specific corpus based on the one or more classification identifiers.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for utilizing temporal indicators to weight semantic values. A set of temporal characteristics is identified of a set of initial candidate answers. For each initial candidate answer in the set of initial candidate answers: a distance value is generated for each of the set of temporal characteristics of the set of initial candidate answers, a multiplier value is determined with which to weight an initial confidence score associated with the initial candidate answer using the distance value; a sentiment value is determined of the initial candidate answer, and a final weight value is determined using the multiplier value, the sentiment value, and the initial confidence score associated with the initial candidate answer. A set of temporally refined candidate answers is then provided using the determined final weight values.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics. Responsive to receiving an input question, a set of candidate answers is identified from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain. A confidence score is associated with each of the candidate answers and each confidence score associated with each candidate answer is refined based on a set of temporal characteristics identified in the input question. A set of temporally refined candidate answers is then provided to the user.

Abstract: A mechanism is provided in a data processing system for applying a genetic algorithm to semantic sentiment analysis. The mechanism provides a sentiment analysis model to a sentiment analysis algorithm. The mechanism trains the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model. The mechanism performs the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result. The mechanism outputs the domain-specific sentiment analysis result.

Abstract: An approach is provided for generating supersets of Q/A action paths based on dynamically generated type sets. In the approach, a corpus of knowledge that is used by the QA system is analyzed. The analyzing is performed according to a natural language processing (NLP) of a number of key words that have been found to exceed an expected frequency, such as a frequency found in general language usage. Runtime code is generated by utilizing the key words. The generated runtime code is able to provide answers from the corpus of knowledge, such as by being in a natural language question or in a structured query language expression, with the provided answers being related to one or more predicted questions.

Abstract: A system, and computer program product for disambiguation of dependent referring expression in natural language processing are provided in the illustrative embodiments. A portion of a document in a set of document is selected, the portion including a set of dependent referring expression instances. The portion is filtered to identify an instance from a set of dependent referring expression instances by using a linguistic characteristic of the instance, the instance of dependent referring expression referring to a full expression occurring in the set of documents. The full expression is located in one member document in the set of documents by locating where the dependent referring expression is defined to be a stand-in for the full expression. The instance is resolved using the full expression such that information about the full expression is available at a location of the instance.

Abstract: A method for disambiguation of dependent referring expression in natural language processing is provided in the illustrative embodiments. A portion of a document in a set of document is selected, the portion including a set of dependent referring expression instances. The portion is filtered to identify an instance from a set of dependent referring expression instances by using a linguistic characteristic of the instance, the instance of dependent referring expression referring to a full expression occurring in the set of documents. The full expression is located in one member document in the set of documents by locating where the dependent referring expression is defined to be a stand-in for the full expression. The instance is resolved using the full expression such that information about the full expression is available at a location of the instance.

Abstract: Provided are techniques for creating a hierarchy of results. An unstructured result set is received. Each result in the unstructured result set is hashed into a preliminary result set. For each hashed result, one or more related concepts are obtained using one or more taxonomies; one or more matches between the one or more related concepts and other hashed results in the preliminary result set are found; a candidate group for the hashed result is formed, wherein the candidate group includes the hashed result and one or more other hashed results based on the one or more matches; in response to determining that a frequency associated with the hashed result exceeds a threshold, the candidate group associated with that hashed result is compared with pre-existing groups that are in use; and, based on the comparing, one or more suggestions regarding the candidate group are provided.