International Business Machines Corporation

David Deidou Taieb

William Graham O'Keeffe

Dan O'Connor

Christopher James Karle

Ashok Kumar

Cale R Vardy

Muhtar B Akbulut

Donna Karen Byron

Srikanth Govindaraj Tamilselvam

Mario Alessandro Maldari

Sabrina Yee

Kenneth M Giffels

Ketan T Patel

Jose Barbosa

Suneil H Berajawala

Lakshminarayanan Krishnamurthy

Scott Harlan Isensee

David R Schwartz

Prithu Banerjee

Robert C Johnson, Jr

Anthony Tom Levas

Swaminathan Chandrasekaran

Mary Swift

Sridhar Sudarsan

Kevin B Haverlock

Siddharth A Patwardhan

Maureen G Leland

Dimple Bhatia

Armageddon Rhabdizo Brown

David P George

Priscilla S Moraes

Brian J Levine

Collin James Murray

Steven D Wood

Richard H Adams, Jr

Thangaraj Veerappan

A method, system and computer-usable medium are disclosed for the use of a text simplification in a question answering (QA) system to improve ingestion quality and QA performance. A source corpus is processed to generate a parsed source corpus, which in turn is processed to generate a shadow corpus of simplified text. The parsed source corpus and the shadow corpus are then processed to generate derived data resources. A user query is processed to generate a set of merged candidate answer variants which are in turn processed to generate a corresponding converged feature vector representing each merged candidate answer variant. Feature values associated with each converged feature vector are then used to rank the merged candidate answer variants. A ranked set of merged candidate answer variants is then provided to the user.

Text simplification for a question and answer system

Aspects include generation of automated response follow-up. A response to a question from a user is received at a response follow-up system. The response follow-up system analyzes the response using natural language processing to identify one or more response terms. The response follow-up system generates one or more follow-up questions based on the one or more response terms. Based on the response, one or more follow-up responses to the one or more follow-up questions are pre-fetched from one or more content provider systems including one or more content servers, where a question answering system including the response follow-up system is communicatively coupled to the one or more content provider systems through a network. The response follow-up system modifies an aspect of a user interface displayed to the user based on the one or more follow-up questions and the one or more follow-up responses.

System for generation of automated response follow-up

Software that answers questions asked of question answering systems, by performing the following steps: (i) identifying a hierarchy of question and answer pairs, where a first answer paired to a first question in the hierarchy of question and answer pairs includes at least a second answer paired to a second question in the hierarchy of question and answer pairs; (ii) receiving, by a question answering system, an input question from a first user; (iii) identifying first user contextual information pertaining to the first user; and (iv) answering the input question, by the question answering system, based, at least in part, on the hierarchy of question and answer pairs and the first user contextual information.

Determining answers to questions using a hierarchy of question and answer pairs

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 receives user input of question text for a question to be submitted to a question answering system in a user interface. The mechanism determines a question strength score for the question text. The question strength score represents a likelihood the question text will result in a correct answer with high confidence. The mechanism presents a graphical representation of the question strength score in the user interface.

Displaying quality of question being asked a question answering system

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions which are executed by the at least one processor and configure the processor to implement a conversation scope system, comprising a conversation scope controller and a conversation scope extractor, for progressively extending conversation scope in a multi-user messaging platform. The conversation scope controller captures an utterance input by a user into a chat platform. The conversation scope extractor determines a conversation scope for the utterance based on at least one entity extracted from the entity. The conversation scope controller directs the utterance to a messaging chat room based on the determined conversation scope.

Progressively extending conversation scope in multi-user messaging platform

A domain specific ontology collection associated with a user is determined. At least one action of the user is received. A task for the user is determined based on the domain specific ontology collection for the user and the at least one action of the user.

Managing a domain specific ontology collection

Aspects include engaging a user in a chat flow by a dialog system. A user sentiment associated with a system response provided by the dialog system as part of the chat flow is determined based on observation of the user. A next system response is rerouted from a planned sequence of the chat flow to a sentiment-based repair sequence to alter content delivered to the user based on a detected aspect of the user sentiment.

Conversation path rerouting in a dialog system based on user sentiment

Aspects include cross validation of user feedback in a dialog system. A repository of previous user feedback associated with a user interface is accessed. One or more identified features in the previous user feedback are classified. A sequence of cross-validation questions is generated in a dialog system to elicit further feedback from a current user of the user interface based on the one or more identified features and observed interactions of the current user with the user interface. Responses to the sequence of cross-validation questions are filtered to group the further feedback associated with the one or more identified features. One or more change suggestions to modify the user interface are stored based on the previous user feedback and the further feedback associated with the one or more identified features.

Cross validation of user feedback in a dialog system

The technique herein substantially improves productivity of Annotator developers by providing methods and systems to develop and test Annotators without having to run a full pipeline every time changes are made to a particular Annotator. To this end, preferably a running pipeline is instrumented to enable automated recording of static configuration and dynamically-generated event data as the pipeline is executed. Based on these data, a reusable data model is generated that captures code and other dependencies in the pipeline (e.g., configuration parameters, intermediary CASes, program flow, annotations, and the like). The data model is then used to facilitate testing of Annotators without using the full pipeline (or even major sub-pipelines therein).

Recording, replaying and modifying an unstructured information management architecture (UIMA) pipeline

Aspects include determination of automated response follow-up. A response to a question is received at a response follow-up system. The response follow-up system analyzes the response using natural language processing to identify one or more response terms. The response follow-up system determines one or more follow-up questions based on the one or more response terms. The response follow-up system modifies an aspect of a user interface based on the one or more follow-up questions.

System for determination of automated response follow-up

Aspects include generation of automated response follow-up. A response to a question is received at a response follow-up system. The response follow-up system analyzes the response using natural language processing to identify one or more response terms. The response follow-up system generates one or more follow-up questions based on the one or more response terms. The response follow-up system modifies an aspect of a user interface based on the one or more follow-up questions.

A mechanism is provided in a data processing system for generating poll information from a chat session. The mechanism monitors chat content in the multiple user chat session. The mechanism identifies a poll question in a message within the chat content. The mechanism identifies a plurality of answers to the poll question in subsequent messages within the chat content. The mechanism generates a poll user interface presenting the poll question and the plurality of answers as selectable options and presents the poll user interface to the multiple users of the chat session.

Generating poll information from a chat session

A computer-implemented method for ranking answers of question-answer pairs in ground truth. The computer-implemented method includes annotating, via an annotation tool, the domain dictionary, including, at least one or more of lexicons, glossaries, knowledge base query for ranking the question-answer pairs. The computer-implemented method further includes defining search patterns of the annotated domain dictionary. The computer-implemented method further includes searching a documents corpus or knowledge based query of the domain dictionary for candidate answers of the question-answer pairs, based on the defined search patterns, the candidate are returned based on the search. The computer-implemented method for evaluating the candidate answers with the annotation tool to verify correctness or acceptability of the returned candidate answers based on the search.

Ranking answers in ground truth of a question-answering system

Backing up electronic data files excluding confidential data. An electronic trigger event for initiating a data backup operation is detected. Data files are identified for backup. One or more files, or parts of a file, are designated for exclusion from the backup process. The backup process initiates a backup operation of one or more files, excluding the designated files.

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Charlestown, MA, United States of America

David Deidou Taieb