Abstract: 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.

Abstract: An embodiment of the invention provides a method wherein a natural language query is received from a user with an interface. An ontological representation of data in a database is received with an input port, including names of concepts and names of concept properties. Template rules are received with the input port, the templates rules being language dependent and ontology independent, the template rules including widely used constructs of a language. Rules are automatically generated with a rule generation engine with the ontological representation of the data in the database and the template rules to identify entities and relations in the natural language query. Entities and relations are identified with a processor, the entities and relations being identified in the natural language query with the rules. The structured data language query is generated with a query generation engine from the entities and relations.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A mechanism is provided in a data processing system for question answering using multi-instance learning. The mechanism trains an answer ranking multi-instance learned model using a ground truth question and answer-key pairs set. When used for answering questions, the mechanism receives an input question from a user and generates one or more candidate answers to the input question. Each of the one or more candidate answers has an associated set of supporting passages. The mechanism determines a confidence value for each of the one or more candidate answers using an answer ranking multi-instance learned model based on the sets of supporting passages. The mechanism ranks the one or more candidate answers by confidence value to form a ranked set of answers, classifies supporting passages to identify the ones which truly support the answer, and presents a final answer from the ranked set of answers, the confidence value for the final answer, and supporting evidence for the final answer to the user.

Abstract: 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.

Abstract: 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.

Abstract: According to an aspect, a candidate token sequence including one or more word tokens is extracted from an unstructured domain glossary that includes entries associated with a domain. A look-up operation is performed to retrieve language data for each word token in the candidate token sequence and annotates each word token in the candidate token sequence found by the look-up operation with corresponding retrieved language data to form an annotated sequence. A pattern match of the annotated sequence is performed relative to a repository of patterns and identifies a best matching pattern from the repository of patterns to the annotated sequence based on matching criteria. The annotated sequence is refined with lexical information associated with the best matching pattern as a refined annotated sequence. The candidate token sequence and the refined annotated sequence are output to a domain-specific computational lexicon file.

Abstract: According to an aspect, a candidate token sequence including one or more word tokens is extracted from an unstructured domain glossary that includes entries associated with a domain. A look-up operation is performed to retrieve language data for each word token in the candidate token sequence and annotates each word token in the candidate token sequence found by the look-up operation with corresponding retrieved language data to form an annotated sequence. A pattern match of the annotated sequence is performed relative to a repository of patterns and identifies a best matching pattern from the repository of patterns to the annotated sequence based on matching criteria. The annotated sequence is refined with lexical information associated with the best matching pattern as a refined annotated sequence. The candidate token sequence and the refined annotated sequence are output to a domain-specific computational lexicon file.

Abstract: An embodiment of the invention provides a method wherein a natural language query is received from a user with an interface. An ontological representation of data in a database is received with an input port, including names of concepts and names of concept properties. Template rules are received with the input port, the templates rules being language dependent and ontology independent, the template rules including widely used constructs of a language. Rules are automatically generated with a rule generation engine with the ontological representation of the data in the database and the template rules to identify entities and relations in the natural language query. Entities and relations are identified with a processor, the entities and relations being identified in the natural language query with the rules. The structured data language query is generated with a query generation engine from the entities and relations.

Abstract: 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.

Abstract: According to an aspect, a candidate lexical kernel unit that includes a word token sequence having two or more words is received. Domain terms that contain the two or more words are retrieved from a terminology resource file of domain terms associated with a domain. The candidate lexical kernel unit and the retrieved domain terms are analyzed to determine whether the candidate lexical kernel unit satisfies specified criteria for use as a building block by a natural-language processing (NLP) tool for building larger lexical units in the domain. Each of the larger lexical units includes a greater number of words than the candidate lexical kernel unit. The candidate lexical kernel unit is identified as a lexical kernel unit based on determining that the candidate lexical kernel unit satisfies the specified criteria. The lexical kernel unit is output to a domain-specific lexical kernel unit file for input to the NLP tool.

Abstract: According to an aspect, a candidate lexical kernel unit that includes a word token sequence having two or more words is received. Domain terms that contain the two or more words are retrieved from a terminology resource file of domain terms associated with a domain. The candidate lexical kernel unit and the retrieved domain terms are analyzed to determine whether the candidate lexical kernel unit satisfies specified criteria for use as a building block by a natural-language processing (NLP) tool for building larger lexical units in the domain. Each of the larger lexical units includes a greater number of words than the candidate lexical kernel unit. The candidate lexical kernel unit is identified as a lexical kernel unit based on determining that the candidate lexical kernel unit satisfies the specified criteria. The lexical kernel unit is output to a domain-specific lexical kernel unit file for input to the NLP tool.

Abstract: An apparatus includes a data processing system for generating and displaying a semantic type concordance. The data processing system includes memory storing a computer program, a display to display data of a concordance generated by the program, and a processor configured to execute the computer program. The computer program includes instructions for displaying a user interface configured to enable a user to select semantic types and specify at least one text document, generating a concordance of the at least one document based on the semantic types, and displaying data of the generated concordance on the display.

Abstract: 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.