Abstract: Generating textual entailment pair by a natural language processing (NLP) system. The NLP system receives two input texts, such as a question and a candidate answer. The NLP system queries a database and retrieves passages likely to include text that support the candidate answer. The NLP system generates parse trees and performs term matching on the passages and scores them according to the matching. The NLP system detects anchor pairs in the question and in the passage and aligns subgraphs (within the parse trees) of one to the other based on matching. The NLP system identifies aligned terms in the question and the passage that are not in the aligned subgraphs. The NLP system identifies text fragments, for the question and the passage, within the non-aligned segments of their respective parse trees, that connect the aligned term to the aligned portion of the subgraph.

Abstract: Generating textual entailment pair by a natural language processing (NLP) system. The NLP system receives two input texts, such as a question and a candidate answer. The NLP system queries a database and retrieves passages likely to include text that support the candidate answer. The NLP system generates parse trees and performs term matching on the passages and scores them according to the matching. The NLP system detects anchor pairs in the question and in the passage and aligns subgraphs (within the parse trees) of one to the other based on matching. The NLP system identifies aligned terms in the question and the passage that are not in the aligned subgraphs. The NLP system identifies text fragments, for the question and the passage, within the non-aligned segments of their respective parse trees, that connect the aligned term to the aligned portion of the subgraph.

Abstract: In configuring a processing system with an application made up of machine learning components, where the application has been trained on a set of training data, the application is executed on the processing system using another set of training data. Outputs of the application produced from the other set of training data identified that concur with ground truth data are identified. The components are adapted to produce outputs of the application that concur with the ground truth data using the identified outputs of the application.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: Provided herein are techniques for processing a context-situated inquiry to provide results satisfying the inquiry. An inquiry and its supporting context are processed using natural language processing to determine an interpretation for the inquiry and context, and the interpretation is presented to receive corrective information for the interpretation. The corrective information is applied to the interpretation and content is retrieved based on the interpretation of inquiry and context to produce candidate results for the inquiry. Supplemental information is iteratively received to generate updated candidate results.

Abstract: Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.

Abstract: A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.

Abstract: A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a timeline generation system, for automated timeline completion. The timeline generation system executing on the processor of the computing device identifies a plurality of events in documents in a corpus of information. The timeline generation system places the plurality of events in a partial timeline data structure. The timeline generation system selects an event progression from an event progression knowledge base. The timeline generation system aligns the selected event progression to the partial timeline data structure. The timeline generation system identifies a set of events missing from the partial timeline data structure. The timeline generation system maps the set of events missing from the partial timeline data structure to the partial timeline based on the selected event progression to form a completed timeline data structure.

Abstract: A computer-implemented method includes detecting a first and second set of citations to a plurality of legal cases cited in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining a first and second set of tones corresponding to each citation in the first and second sets of citations, respectively. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones, respectively. The computer-implemented method further includes detecting a first set of annotations corresponding to the first set of citations. The computer-implemented method further includes building a training model, based, at least in part, from at least a subset of the first set of annotations. The computer-implemented method further includes determining a second set of annotations corresponding to the second set of citations.

Abstract: Provided herein are techniques for processing a context-situated inquiry to provide results satisfying the inquiry. An inquiry and its supporting context are processed using natural language processing to determine an interpretation for the inquiry and context, and the interpretation is presented to receive corrective information for the interpretation. The corrective information is applied to the interpretation and content is retrieved based on the interpretation of inquiry and context to produce candidate results for the inquiry. Supplemental information is iteratively received to generate updated candidate results.

Abstract: In configuring a processing system with an application made up of machine learning components, where the application has been trained on a set of training data, the application is executed on the processing system using another set of training data. Outputs of the application produced from the other set of training data identified that concur with ground truth data are identified. The components are adapted to produce outputs of the application that concur with the ground truth data using the identified outputs of the application.

Abstract: A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

Abstract: A computer-implemented method includes detecting a first and second set of citations to a plurality of legal cases cited in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining a first and second set of tones corresponding to each citation in the first and second sets of citations, respectively. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones, respectively. The computer-implemented method further includes detecting a first set of annotations corresponding to the first set of citations. The computer-implemented method further includes building a training model, based, at least in part, from at least a subset of the first set of annotations. The computer-implemented method further includes determining a second set of annotations corresponding to the second set of citations.

Abstract: A computer-implemented method includes detecting a first and second set of citations to a plurality of legal cases cited in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining a first and second set of tones corresponding to each citation in the first and second sets of citations, respectively. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones, respectively. The computer-implemented method further includes detecting a first set of annotations corresponding to the first set of citations. The computer-implemented method further includes building a training model, based, at least in part, from at least a subset of the first set of annotations. The computer-implemented method further includes determining a second set of annotations corresponding to the second set of citations.

Abstract: An embodiment of the invention provides a method for including receiving a natural language query from a user with an interface, and generating multiple dependency parses of the natural language query with a parser device connected to the interface. The generating of the multiple dependency parses includes dividing the natural language query into multiple components, and creating a single dependency parse by connecting each component of the components with at least one other component of the components. A processor connected to the parser device applies rules to all of the multiple dependency parses to identify entities and relations in the natural language query.

Abstract: Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.

Abstract: A contextually relevant patient information extractor is provided that receives an input question directed to medical information about a patient; analyzes a patient's electronic medical records (EMRs) to identify an initial entry in the patient's EMRs corresponding to a candidate answer to the input question; analyzes a context of the patient's EMRs based on the initial entry to identify entries in the patient's EMR that are contextually connected to the initial entry; performs question answering analysis on the initial entry and entries that are contextually connected to the initial entry to identify one or more candidate answers to the input question; and outputs a final answer to the input question based on the question answering analysis.