International Business Machines Corporation

Amit P Bohra

Krishna Kummamuru

Abhishek Shivkumar

Anand B Bhagwat

Swapnasarit Sahu

Abhishek Shrivastava

Naveen G Balani

Manojkumar Pal

Alexander Pikovsky

Donna Karen Byron

A set of first results from a first query are received. Using a natural language processing technique, a set of second results using a second query having at least a portion of the first query and at least a portion of set of first results are generated, wherein the natural language processing technique comprises a machine learning model configured to analyze the set of first results. The set of second results are scored based upon a semantic comparison of the set of first results, the second query, and the set of second results. Using the scored set of the second results, a set of processed results are generated.

Managing a search

A method for generating an appendix from document content is provided. Included is analyzing a structure of a document to identify at least one logical element in a content of a page of the document; identifying at least one relation within the identified at least one logical element to determine an appendix topic; determining a degree of relevance of the at least one relation identifying the appendix topic; extracting an appendix content from at least one source document, based on determining a relevance of the source document to the appendix topic; and formatting the appendix based on the extracted appendix content.

Analyzing document content and generating an appendix

A mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

Determining comprehensiveness of question paper given syllabus

A mechanism is provided in a data processing system for automatically generating question and answer pairs for training a question answering system for a given domain. The mechanism identifies a set of patterns of components in passages within a corpus of documents for the given domain. The mechanism identifies a set of rules that correspond to the set of patterns for generating question and answer pairs from the passages within the corpus of documents. The mechanism applies the set of rules to the passages to generate the question and answer pairs.

Automatically generating test/training questions and answers through pattern based analysis and natural language processing techniques on the given corpus for quick domain adaptation

A method and system for managing a search is disclosed. The method and system can include receiving a set of first results from a first query. A set of categories can be identified based on the set of first results. The method and system can include generating a set of second results using a second query having at least a portion of the first query, at least a portion of the first results, and at least a portion of the set of categories. The method and system can also include scoring the set of second results based upon a relationship of the first query, the second query, and the set of second results.

A computer implemented method of generating a language section from tabular data in an electronic document may include identifying, in a first tabular portion of the electronic document, a set of categories used to organize tabular data. The method may include identifying a content characteristic for each category of the set of categories in the first tabular portion. And the method may include generating a first language section from at least two distinct categories of the set of categories, wherein a format of the first language section is based on the content characteristics for the at least two distinct categories.

Generating language sections from tabular data

A computing device receives one or more questions. The computing device determines at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions and determines at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions. The computing device determines an incentive based on the determined at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions, and the determined at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions.

Determining incentive for crowd sourced question

A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

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Pune, India

Amit P Bohra