International Business Machines Corporation

Douglas A Smith

Malous Melissa Kossarian

Charles E McManis, Jr

Jennifer Ann English

William S Ko

Bryn Robert Dole

Huaiyu Zhu

Morgana M Trexler

Zhiyong Xia

Marcia W Patchan

Christopher M Hoffman, Jr

Matthew Yeager

A nanofiber comprising a polyamide including at least one substituted phenyl group is provided. The nanofiber includes an average diameter from about 50 to about 1000 nm. A fibrous mat including a plurality of the nanofibers is also provided. A composite including a plurality of the nanofibers and a continuous matrix resin is also provided. A method of forming the nanofibers is also provided.

Electrospun nanofibers

According to one embodiment, a computer program product for identifying key words within a plurality of documents comprises a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, and where the program instructions are executable by a processor to cause the processor to perform a method comprising identifying, by the processor, a first group of textual data, determining, by the processor, a plurality of word combinations within the first group of textual data, and determining, by the processor, a first plurality of key words for the first group of textual data, utilizing the plurality of word combinations.

Identifying key words within a plurality of documents

A computer-implemented method according to one embodiment includes identifying a first group of textual data, determining a plurality of word combinations within the first group of textual data, and determining a first plurality of key words for the first group of textual data, utilizing the plurality of word combinations.

A technique for answering questions includes receiving a question directed to a first subject. A mathematical operation is performed between each of one or more first topic vectors (associated with the first subject) and each of one or more second topic vectors (associated with a second subject) to generate respective strength values. Relevant ones of the respective strength values are summed to provide an overall strength value, which is utilized to determine a semantic distance (SD) between the first subject and the second subject. In response to the SD being within a threshold distance value (TDV), information associated with the first subject and the second subject is utilized to answer the question. In response to the SD not being within the TDV, information associated with the first subject is utilized to answer the question.

Answering questions based on semantic distances between subjects

A computer-implemented method according to one embodiment includes identifying a plurality of documents associated with a predetermined subject, where each of the plurality of documents contains textual data, analyzing the textual data of each of the plurality of documents to identify one or more categories within the plurality of the documents, and returning the one or more categories identified within the plurality of the documents.

Identifying categories within textual data

A technique for calculating a semantic distance between subjects includes performing a mathematical operation between each of one or more first topic vectors and each of one or more second topic vectors to generate respective strength values. The first topic vectors are associated with respective first topics of a first subject, the second topic vectors are associated with respective second topics of a second subject, and the respective strength values are indicative of a relative closeness between associated ones of the first and second topics. Relevant ones of the respective strength values are summed to provide an overall strength value between the first subject and the second subject. A semantic distance between the first subject and the second subject is determined based on the overall strength value.

Techniques for determining a semantic distance between subjects

Determining a semantic distance between subjects

A training query generation system is usable to generate fully formed training questions from prior search queries, some of which may be fully formed search queries and some of which are not fully formed. The system may identify fully formed questions from a query database stored on a storage device. The query database includes a plurality of search query character string, The system further identifies partially formed questions from the query database, creates question templates from the identified fully formed questions, and stores the question templates in a template database. The system then identifies entities with the partially formed questions, classifies each entity, and stores the classified entities in an entity database. Fully formed questions can then be generated using the question templates from the template database and classified entities from the entity database.

Natural question generation from query data using natural language processing system

Embodiments provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a full question generation system, the method comprising ingesting a query dataset derived from one or more search queries entered by one or more users of an internet search engine; identifying questions from the ingested query dataset; separating one or more prior full questions from the ingested dataset; identifying one or more question intent queries from the query dataset; for each identified question intent query; sorting the question intent query into one or more bins based on one or more missing interrogative words; and appending the missing interrogative word and a verb onto the question intent query to generate a full question. The full question generation method can additionally generate un-canonical questions.

System and method for generating full questions from natural language queries

Embodiments provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a full question generation system, the method comprising ingesting, into the full question generation system, a query dataset derived from one or more search queries entered by one or more users of an internet search engine; identifying questions from the ingested query dataset; separating, through a full question identification module, one or more prior full questions from the ingested dataset; identifying, through a question intent query identification module, one or more question intent queries from the query dataset; for each identified question intent query: sorting, through a sorting module, the question intent query into one or more bins based on one or more missing interrogative words; and appending, through an appending module, the missing interrogative word and a verb onto the question intent query to generate a full question; and ingesting, through the cognitive system, the one or more generated full questions as training dataset. The full question generation method can additionally generate un-canonical questions.

Embodiments provide a system and method for semantic distance calculation. The method can involve receiving a plurality of documents having a set of subjects extracted through the use of latent dirichlet allocation; for each document in the plurality of documents, generating a classification list comprising a ranking of the one or more subjects based on the relevance of each subject to the document; for each classification list, calculating the semantic distance between each subject present on the classification list; aggregating the plurality of classification lists; and creating a distance matrix containing the relative semantic distances between each member of the set of subjects.

Phenomenological semantic distance from latent dirichlet allocations (LDA) classification

Embodiments provide a system and method for semantic distance calculation. The method can involve ingesting a plurality of documents; extracting a set of subjects from the plurality of documents using latent dirichlet allocation; for each document in the plurality of documents, generating a classification list comprising a ranking of the one or more subjects based on the relevance of each subject to the document; for each classification list, calculating the semantic distance between each subject present on the classification list; aggregating the plurality of classification lists; and creating a distance matrix containing the relative semantic distances between each member of the set of subjects.

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Sunnyvale, CA, United States of America

Douglas A Smith