Abstract: Mechanisms are provided for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines. The mechanisms receive an input question for processing by the QA system and determine a predicted question difficulty of the input question. The mechanisms select a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty and route the input question to the selected QA system pipeline for processing. In addition, the mechanisms process the input question with the selected QA system pipeline to generate an answer for the input question.

Abstract: A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score.

Abstract: Mechanisms for automatically generating a set of test questions for use in generating a test to be administered to human test takers are provided. The mechanisms ingest a document from a corpus of documents to select a potential test question answer from the document and generate a candidate test question based on the potential test question answer. The mechanisms evaluate the candidate test question using a Question and Answer (QA) system to generate a result indicative of whether the candidate test question should be maintained for test generation. The mechanisms store the candidate test question in the set of test questions in response to a result indicating that the candidate test question should be maintained for test generation.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Abstract: Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Abstract: Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

Abstract: Aspects of processing misaligned annotations include receiving a tokenized document and offset annotation file at a processor. The tokenized document includes a source document and corresponding tokens resulting from a low-level segmentation process. Annotations from the annotation file are applied, in conjunction with tokenization rules, to the source document, and a misalignment responsive to the applying is determined. If the misalignment is caused by an offset mismatch, an offset number of characters between the position counts in the annotation file and the source document is calculated, and the position count in the annotation file is adjusted to coincide with the position count in the source document. If the misalignment is not caused by an offset mismatch, a current position count in the source document is reset to a position count of a previous location in which a most recent alignment between the annotation file and the source document was ascertained.

Abstract: Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

Abstract: Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

Abstract: Aspects of processing misaligned annotations include receiving a tokenized document and offset annotation file at a processor. The tokenized document includes a source document and corresponding tokens resulting from a low-level segmentation process. Annotations from the annotation file are applied, in conjunction with tokenization rules, to the source document, and a misalignment responsive to the applying is determined. If the misalignment is caused by an offset mismatch, an offset number of characters between the position counts in the annotation file and the source document is calculated, and the position count in the annotation file is adjusted to coincide with the position count in the source document. If the misalignment is not caused by an offset mismatch, a current position count in the source document is reset to a position count of a previous location in which a most recent alignment between the annotation file and the source document was ascertained.

Abstract: A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score.

Abstract: Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

Abstract: Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Abstract: A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score.

Abstract: Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.