Abstract: Embodiments provide a computer implemented method for ranking supporting passages in a question answering (QA) system. The method includes the following steps: receiving an input question and a plurality of passages, wherein the plurality of passages are extracted from the document corpus by the QA system; identifying a plurality of candidate answer occurrences from the plurality of passages; assigning a plurality of first local features to each candidate answer occurrence; merging the plurality of candidate answer occurrences to generate a group of candidate answers; assigning a plurality of second local features and a plurality of nonlocal features to each candidate answer; ranking the group of candidate answers using a linear answer scoring model; and ranking the plurality of candidate answer occurrences using the linear answer scoring model.

Abstract: Mechanisms are provided for implementing a proximity based candidate answer pre-processor engine that outputs a sub-set of candidate answers to a question and answer (QA) system. The mechanisms receive a lexical answer type (LAT) and an entity specified in an input natural language question as well as an ontology data structure representing a corpus of natural language content. The mechanisms identify a set of candidate answers having associated nodes in the ontology data structure that are within a predetermined proximity of a node corresponding to the entity, and a sub-set of candidate answers in the set of candidate answers having an entity type corresponding to the LAT. The mechanisms output, to the QA system, the sub-set of candidate answers as candidate answers to the input natural language question for evaluation and selection of a final answer to the input natural language question.

Abstract: Embodiments relate to an intelligent computer platform to use ground truth data to rate source reliability, and application of the source reliability. A computer system, computer program product, and computer implemented method apply the ground truth data to evaluate a source, assess source reliability, and dynamically apply the assessed reliability. The assessed reliability is dynamically applied to response data, with the application yielding selective presentation of the response data based on the applied reliability assessment.

Abstract: A method trains a neural network to identify an event based on discrepancies in answers to factoid questions at different times. One or more processors identify answers to a series of factoid questions. The processor(s) compare the answers from the series of factoid questions in order to determine discrepancies in the answers at different times, and then train a neural network to identify an event based on the discrepancies in the answers at the different times.

Abstract: A method learns approximate translations of unfamiliar measurement units during deep question answering (DeepQA) system training and usage. The DeepQA system receives a training set containing Question-Answer (QA) pairs having known unit-of-measurement terms, where each QA pair contains an answer having a known numeric value for a corresponding question from the QA pair. The DeepQA system receives a question from each QA pair from the training set to the DeepQA system in order to find answers and passage phrases to the question from each QA pair, and then identifies all found answers and passage phrases having values that are within a predetermined range of answer values of the training set, where one or more of the identified all found answers and passage phrases contain unfamiliar unit-of-measurement terms, in order to learn approximate translations of the unfamiliar unit-of-measurement terms.

Abstract: Embodiments for managing the utilization of software releases are provided. Information associated with a software release and at least one early adopter of the software release is analyzed to calculate a severity score for the software release. A time to utilize the software release is determined based on the calculated severity score.

Abstract: A question and answer (QA) system, computer program product, and computer-implemented method configured to determine an answer to a question that includes a measurement value. In one example, the QA system receives a question and analyzes the question to identify a measurement value specified in the question. The QA system determines relevant passages to the question. The QA system assigns a measurement value confidence score to a relevant passage based on a comparison of the measurement value specified in the question and a second measurement value specified in the relevant passage. The QA system determines an order of the relevant passages using the measurement value confidence score of each of the relevant passages. The QA system determines an answer to the question based on the order of the relevant passages.

Abstract: A method of augmenting an information retrieval method including receiving a query, parsing the query to generate an instance graph, providing a plurality of constraint graphs, organized into at least two ranks, each constraint graph associated with a focus, determining at least one of the constraint graphs matching a structure of the instance graph, and annotating the query with the focus of each matching constraint graph of a highest rank among all matching constraint graphs.

Abstract: A method utilizes a deep question answering (QA) system to provide an answer, to a certain type of question, that includes an unfamiliar measurement unit. An answer key is utilized to train a DeepQA system to search for passages that answer a certain type of question, where the DeepQA system outputs an answer key value and an answer key measurement unit that is associated with the answer key value. The method identifies a candidate answer that includes a candidate passage containing the answer key value but not the answer key measurement unit, where a candidate passage measurement unit in the candidate passage is associated with the answer key value. The method then matches the answer key measurement unit to the candidate passage measurement unit based on the answer key measurement unit and the candidate passage measurement unit both being associated with the answer key value.

Abstract: A system and a method for determining user synonyms in a query processing system is disclosed. A first query is received from a user of the query processing system. The query is processed through the query processing system to generate results for the first query. The user then provides a second query. The system determines a contextual similarity between the first query and the second query. When the first query and the second query are determined to be contextually similar, a first term is identified in the first query that is different from a second term in the second query. Once identified, the system determines if the first term and the second term are not listed as synonyms in the thesaurus. If they are not listed as synonyms they can be added as synonyms to the thesaurus.

Abstract: Embodiments relate to an intelligent computer platform to identify and evaluate candidate passage response data in natural language form. Natural language processing is applied to analyze a passage against one or more input tokens to identify matching content. A structure representing the analyzed passage is populated with matching input and passage tokens. A first count of matching token entries and a second count of evaluated token entries are determined and qualified by closeness criteria. An alignment of the passage to a candidate question is calculated, including assessing a ratio of the first and second counts as a confidence value. Matching passage data is returned from the passage with the confidence value.

Abstract: A method trains and utilizes an artificial intelligence (AI) system. The AI system receives a question that has contextual features. The method trains the AI system to identify entries in a corpus that have one or more of the contextual features from the question. The method further trains the AI system to: form a set of answers to the question based on identified contextual entries in the corpus; identify and name an entry in the corpus that has a highest quantity of contextual features that match the contextual features in the question as an initial answer to the question; identify and merge multiple other answers to the question from the corpus; and replace the initial answer with the merged answer in order to create a fully trained AI system. The fully trained AI system is then utilized to answer the question with the merged answer.

Abstract: Embodiments relate to a computer platform and corresponding process and program code to assess multimedia files with respect to similarity and duplicate media content. Data streams are converted into sequences of events, and object representation within the streams is identified and subject to processing with respect to the event sequences. A similarity assessment is conducted between two or more of the data streams, and a corresponding distance measurement to quantify similarity is produced. Duplicate data is selectively identified in response to the similarity assessment and the produced distance measurement.

Abstract: A method determines a relevancy of answers to questions based on token relevance in a system capable of answering questions. One or more processors receive a question that is composed of a set of tokens T (T1, T2, . . . , Tn). The processor(s) select tokens T? (T?1, T?2, . . . , T?m) from the tokens T (T1, T2, . . . , Tn), where each T?j from T? is a noun, and classify each T?j as a noun type. The processor(s) scan a corpus to identify passages with candidate answers to the question, and analyze the identified passages utilizing noun entries in the passages classified as the noun type. The processor(s) train an artificial intelligence (AI) system to associate a relevancy to the question for the identified passages based on noun types, and then utilize the trained AI system to provide an answer to the question based on an output of the trained AI system.

Abstract: The present invention may receive the question, a plurality of the candidate answers, and a plurality of documents associated with the plurality of candidate answers in the natural language. Then the present invention may tokenize the question, the plurality of the candidate answers, and the plurality of the documents into a corresponding n-gram sequence. The present invention may map n-gram elements from the tokenized question to the n-gram elements of the plurality of the tokenized candidate answers and the plurality of the tokenized documents using the latent token representation technique. The present invention may score the plurality of tokenized candidate answers based on the latent token representation technique. Then, the present invention may determine the precise answer based on the plurality of scored candidate answers.

Abstract: Embodiments relate to an intelligent computer platform to use ground truth data to rate source reliability, and application of the source reliability. A computer system, computer program product, and computer implemented method apply the ground truth data to evaluate a document and statements therein, and dynamically apply the assessed reliability. The assessed reliability is dynamically applied to a source, with the application yielding acceptance of the document to the source based on the applied reliability assessment.

Abstract: A service, in response to receiving a question in a natural language format, identifies one or more selected passages from a corpus that are relevant to a focus of the question from among multiple passages in the corpus. The service aligns one or more answer grammatical properties of one or more answers, selected from the one or more selected passages, to one or more question grammatical properties of the focus of the question. The service returns the one or more answers in response to the question.

Abstract: A method for providing unsupervised entity resolution to a natural language processing system includes receiving a named entity for training from the natural language processing system, searching a corpus for a first undisambiguated named entity corresponding to the named entity, identifying a plurality of disambiguated named entities corresponding to the first undisambiguated named entity, identifying a plurality of aliases for each of the disambiguated named entities, training a classifier for the each of the disambiguated named entities utilizing the aliases identified for respective ones of the disambiguated named entities using the corpus, and resolving the named entity using the classifier, wherein resolving the named entity comprises selecting one of the disambiguated named entities from among the disambiguated named entities and returning, automatically, the selected disambiguated named entity to the natural language processing system.

Abstract: A mechanism is provided for routinely evaluating an accuracy of a request processing pipeline. A set of questions is executed through the request processing pipeline, producing a list of answers, supporting documents, and accuracy metrics. A determination is made as to whether a document contribution value of each document associated with the answer is equal to or above a document contribution threshold value. For those documents equal to or above the document contribution threshold value, a snapshot is stored in a training-data data structure. Based on a clustering of questions, for each question cluster, a determination is made of an average accuracy metric. A comparison is performed and a determination is made as to whether an accuracy metric delta exceeds the accuracy metric threshold value. If so, a differential report is generated indicating a review is needed of a training of the request processing pipeline.

Abstract: Embodiments relate to an intelligent computer platform to identify and evaluate candidate passages response data in natural language form. A passage is analyzed by applying input tokens against passage tokens. A structure representing the analyzed passage is populated with of matching input and passage tokens. The input and passage tokens are used to assess the structure. First and second counts of token matching criteria are determined, including a first count of matching token entries and a second count of evaluated token entries. An alignment of the passage to a candidate question is calculated and a ratio of the first and second counts is assessed. Data from the passage are returned with the ratio.