International Business Machines Corporation

Branimir Konstantinov Boguraev

Benjamin P Segal

David Angelo Ferrucci

Anthony Tom Levas

Siddharth A Patwardhan

Esme Manandise

Jennifer Chu-Carroll

James W Murdock, Iv

Aditya Anand Kalyanpur

Bharath Dandala

Lakshminarayanan Krishnamurthy

Youssef Drissi

Paul Turquand Keyser

Roy Jefferson Byrd

Mary Susan Neff

Vadim Sheinin

Eric William Brown

Petros Zerfos

John Martin Prager

David W Buchanan

James William Cooper

Dafna Sheinwald

Elahe Khorasani

Robert Bruce Mahaffey

Catherine Keefauver Laws

Jani Gabriel Byrne

David J McClosky

Rie Kubota Ando

Swaminathan Chandrasekaran

Kenneth James Barker

Mihaela Ancuta Bornea

Sugato Bagchi

John P Bufe, Iii

Anni Rosa Coden

Wilfried Teiken

Sara Rosenthal

Jared M D Smythe

Keh-Shin Fu Cheng

Adam R Faulkner

Michael A Tanenblatt

Matthew T Hatem

Esme Manadise

Yanpeng Li

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.

Entailment knowledge base in natural language processing systems

An approach is provided to receive, at a question answering (QA) system, a question and identify a politeness corresponding to a number of terms corresponding to the question that are included in a corpus of the QA system. The approach identifies the politeness of one or more terms included in each of a set of candidate answers responsive to the question. Finally, the approach scores each of the candidate answers, with the scoring being based, in part, on the politeness identified for each of the terms.

Augment politeness in question answer systems

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.

Domain-specific lexical analysis

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.

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.

Processing context-based inquiries for knowledge retrieval

Natural language processing (NLP) with awareness of textual polarity. An NLP system, such as a search engine or a Question-Answering (QA) system receives input text for processing. The input text may be a text fragment, a search phrase, a question having a general type, or a polar question having a yes or no answer. The NLP system identifies textual polarity and provides responses to the input text (for example, in answer form) based on identifying evidence whose selection, scoring, and processing, is informed by the textual polarity of the input text, and the textual polarity of candidate evidence passages.

Natural language processing based on textual polarity

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.

Question correction and evaluation mechanism for a question answering system

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.

Generating secondary questions in an introspective question answering system

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.

Domain-specific lexically-driven pre-parser

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.

Annotations can be handled by a computer system that receives a query that specifies parameters for extraction of particular annotations from a set of annotations. Annotations include metadata that describes properties of the associated text fragment. A first entity subset, a second entity subset and a relations subset of annotations are extracted from an annotated text corpus. Contextual information relative to the extracted annotations is also extracted from the corpus. A user interface is generated to display frame elements that include the extracted annotations subsets and the extracted contextual information. In response to selections to the frame elements, the system receives input that specifies modifications to the annotations. Based on the input received, the set of annotations is modified in the annotated text corpus.

System for retrieving, visualizing and editing semantic annotations

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.

Robust and readily domain-adaptable natural language interface to databases

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Bedford, NY, United States of America

Branimir Konstantinov Boguraev