Abstract: According to an aspect, a processing system of a question answering computer system determines a first set of relations between one or more pairs of terms in a question. The processing system also determines a second set of relations between one or more pairs of terms in a candidate passage including a candidate answer to the question. The processing system matches the first set of relations to the second set of relations. A plurality of scores is determined by the processing system based on the matching. The processing system aggregates the scores to produce an answer score indicative of a level of support that the candidate answer correctly answers the question.

Abstract: Systems, methods, and computer-readable media for integrating e-commerce capabilities with social media services using a central trusted service are disclosed. A client application executable on a client device interacts with a central trusted e-commerce/social media service executing on one or more servers. The central trusted service receives social networking credentials associated with a user from the client application and identifies a particular user profile associated with the user based at least in part on the received social networking credentials. The central trusted service determines a trusted group of user profiles linked to the particular user profile and analyzes various types of data associated with the trusted group of user profiles to identify products to recommend to the particular user profile. In addition, the central trusted service provides a direct, centralized conduit to online retailers to allow the user to purchase a product via a trusted and secure mechanism.

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: A mechanism is provided in a data processing system for using paraphrase metrics for answering questions. The mechanism receives an input question and generating a candidate answer from a corpus of information. The candidate answer has a supporting passage from the corpus of information. The mechanism divides the input question into a first sequence of tokens and divides the supporting passage into a second sequence of tokens. The mechanism identifies a plurality of subsequences of tokens within the second sequence of tokens and applies a paraphrase metric to compare the first sequence of tokens to each of the plurality of subsequences of tokens to generate a plurality of paraphrase metric scores. The mechanism then determines a confidence score for the candidate answer based on a highest paraphrase metric score within the plurality of paraphrase metric scores.

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: 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: A mechanism is provided in a data processing system for using paraphrase metrics for answering questions. The mechanism receives an input question and generating a candidate answer from a corpus of information. The candidate answer has a supporting passage from the corpus of information. The mechanism divides the input question into a first sequence of tokens and divides the supporting passage into a second sequence of tokens. The mechanism identifies a plurality of subsequences of tokens within the second sequence of tokens and applies a paraphrase metric to compare the first sequence of tokens to each of the plurality of subsequences of tokens to generate a plurality of paraphrase metric scores. The mechanism then determines a confidence score for the candidate answer based on a highest paraphrase metric score within the plurality of paraphrase metric scores.

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 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: A computer implemented program product and data processing system for receiving data to a targeted logical partition. A computer locates buffer element in reliance on a connection status bit array. The computer copies control information to the targeted logical partition's local storage. The computer updates a targeted logical partition's local producer cursor based on the control information. The computer copies data to an application receive buffer. The computer determines that an application completes a receive operation. Responsive to a determination that the application completed the receive operation, the computer a targeted logical partition's local consumer cursor to match the targeted logical partition's producer cursor.

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: A mechanism is provided in a data processing system for using paraphrase metrics for answering questions. The mechanism receives an input question and generating a candidate answer from a corpus of information. The candidate answer has a supporting passage from the corpus of information. The mechanism divides the input question into a first sequence of tokens and divides the supporting passage into a second sequence of tokens. The mechanism identifies a plurality of subsequences of tokens within the second sequence of tokens and applies a paraphrase metric to compare the first sequence of tokens to each of the plurality of subsequences of tokens to generate a plurality of paraphrase metric scores. The mechanism then determines a confidence score for the candidate answer based on a highest paraphrase metric score within the plurality of paraphrase metric scores.

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.