Abstract: Clustering a set of natural language queries NLQs based on a set of significant events retrieved from a corpus stored in a computer system is described. A set of NLQs is used by a search engine for searching a selected corpus to retrieve respective sets of significant events. The set of NLQs is clustered into a plurality of NLQ clusters according to a threshold number of common significant events being returned by the search engine for respective members of an NLQ cluster.

Abstract: A system includes a question answering system executed by a computer, a processor, and a memory coupled to the processor. The memory is encoded with instructions that when executed cause the processor to provide training for training the question answering system. The training system is configured to receive a plurality of bidirectional disjunctive logical forms which include two directional disjunctions of differences between a first logical form of a first sentence and second logical form of a second sentence, realize the plurality of bidirectional disjunctive logical forms to generate a first plurality of paraphrases of the first and second sentence, score each of the first plurality of paraphrases based on textual similarity between the first plurality of paraphrases and the first and second sentences, and prune the first plurality of paraphrases to generate a second plurality of paraphrases based on the scores of each of the first plurality of paraphrases.

Abstract: A method, system and a computer program product are provided for collecting document segmentation data by activating a document segmentation collection browser plugin with a designated toolbar button to generate one or more initial document segments from a webpage document and to receive user feedback for modifying a first initial document segment through a document segment control tool to generate a modified set of one or more initial document segments which are stored as document and document preprocessing data for the webpage document.

Abstract: A natural language query (NLQ) is translated to a structured data query (e.g., a SQL statement) by extracting entities from the NLQ and replacing them with generic variables to form a generic query. The generic query is associated with a structured question type which includes structured data variables using natural language classifiers (NLCs). Specific data is inserted in the structured question type in relation to the structured data variables based on the extracted entities to form the structured data query. An ensemble of NLCs trained with different ground truths can be used to yield multiple candidate question types. One of the candidate question types is selected based on confidence levels. The multiple NLCs can include an NLC which is optimized according to a focus of the generic query. For example, an NLC can be optimized for a specific data structure (such as SQL), or for comparative queries.

Abstract: A natural language query (NLQ) is translated to a structured data query (e.g., a SQL statement) by extracting entities from the NLQ and replacing them with generic variables to form a generic query. The generic query is associated with a structured question type which includes structured data variables using natural language classifiers (NLCs). Specific data is inserted in the structured question type in relation to the structured data variables based on the extracted entities to form the structured data query. An ensemble of NLCs trained with different ground truths can be used to yield multiple candidate question types. One of the candidate question types is selected based on confidence levels. The multiple NLCs can include an NLC which is optimized according to a focus of the generic query. For example, an NLC can be optimized for a specific data structure (such as SQL), or for comparative queries.

Abstract: A method, system and computer-usable medium are disclosed for preserving temporal relevance of content within a corpus. A corpus is processed to generate temporally-relevant metadata and mined content, which in turn are processed to generate first temporal relevancy metrics. The cache history of a web browser is likewise processed to generate second temporal relevancy metrics, which in turn is processed with the first temporal relevancy metrics to generate first temporal relevancy scores. New documents are ingested into the corpus and existing documents are revised. Temporally-relevant metadata and mined content associated with the updated corpus are then processed to generate third temporal relevancy metrics. The second and third temporal relevancy metrics are then processed to generate second temporal relevancy scores, which is then used to provide a temporally-relevant response to a query.

Abstract: A method, system and computer-usable medium are disclosed for preserving temporal relevance in a response to a query. A query is processed to extract first temporal features, which is then used to identify first documents within a corpus. The first documents are processed to generate first metadata and mined content, which is processed with the first temporal features to generate second documents having second temporal features corresponding to the first temporal features. The corpus is updated with new documents and revisions to the first documents to generate second documents. In turn, the second documents are processed to generate second metadata and mined content, which is then used to generate a ranked list of temporally-relevant documents.

Abstract: A search space can be reduced using a pruned ontology that comprises entities from an initial ontology. A document corpus having a plurality of documents is received for processing. An ontology pruner determines a set of entities in the plurality of documents. For each entity in the set of entities, the ontology pruner determines a link for the entity in the initial ontology. The ontology pruner determines a score for the entity based on information obtained through the link for the entity in the initial ontology. The ontology pruner omits the entity from the pruned ontology if the score is below a predetermined or configurable threshold value.

Abstract: A system includes a question answering system executed by a computer, a processor, and a memory coupled to the processor. The memory is encoded with instructions that when executed cause the processor to provide training for training the question answering system. The training system is configured to receive a first phrase and a second phrase, the first and second phrases being paraphrases of each other, convert the first phrase into a first logical form and the second phrase into a second logical form, generate a phrasal edit that includes a difference between the first logical form and the second logical form, convert the phrasal edit into a disjunctive logical form in two directions, and generate a first plurality of paraphrases of the first and second phrases based on the disjunctive logical form.

Abstract: A system includes a question answering system executed by a computer, a processor, and a memory coupled to the processor. The memory is encoded with instructions that when executed cause the processor to provide training for training the question answering system. The training system is configured to receive a plurality of bidirectional disjunctive logical forms which include two directional disjunctions of differences between a first logical form of a first sentence and second logical form of a second sentence, realize the plurality of bidirectional disjunctive logical forms to generate a first plurality of paraphrases of the first and second sentence, score each of the first plurality of paraphrases based on textual similarity between the first plurality of paraphrases and the first and second sentences, and prune the first plurality of paraphrases to generate a second plurality of paraphrases based on the scores of each of the first plurality of paraphrases.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: A method, system and computer-usable medium are disclosed for using a contextual graph to summarize a corpus of content. Natural Language Processing (NLP) preprocessing operations are performed on text within an input corpus to form a grammatical analysis. In turn, the grammatical analysis is used to generate semantic associations between phrases in the input corpus. The resulting semantic associations are then used to determine the thematic relevance of the individual sentences in the input corpus to form a context-based ranking. In turn, the context-based ranking is used to construct a context graph, the vertices of which are represented by phrases, and the edges are represented by an aggregate score resulting from performing calculations associated with semantic similarity of the phrases. The resulting context graph is then used to generate a content summarization for the input corpus.

Abstract: A method, system and computer-usable medium are disclosed for generating a context-sensitive summarization of a corpus of content. Natural Language Processing (NLP) operations are performed on text within an input corpus to extract phrases, which are then used to generate a grammatical analysis. In turn, the grammatical analysis is used to determine the thematic relevance of individual sentences in the input corpus. Sentences within the input corpus are then ranked according to their respective thematic relevance. This ranking is used to construct a contextualized content graph, which in turn is used to generate a content summarization for the input corpus.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: A computer-implemented method includes creating a classifier by: training a machine learning model using two or more tasks, wherein the tasks lie in two or more domains; including in the machine learning model at least one attribute common to at least two of said two or more domains; including in the machine learning model at least one latent feature that affects at least two of the two or more tasks that fall within one of the at least two domains; and constructing the classifier based on said machine learning model. The computer-implemented method further includes applying the classifier to at least one operational task.

Abstract: A computer-implemented method includes creating a classifier by: training a machine learning model using two or more tasks, wherein the tasks lie in two or more domains; including in the machine learning model at least one attribute common to at least two of said two or more domains; including in the machine learning model at least one latent feature that affects at least two of the two or more tasks that fall within one of the at least two domains; and constructing the classifier based on said machine learning model. The computer-implemented method further includes applying the classifier to at least one operational task.