International Business Machines Corporation

Other

Green Market Square Limited

Afrl/rij

James J Fan

David Angelo Ferrucci

Aditya Anand Kalyanpur

Chang Wang

Bing Xiang

Bowen Zhou

James W Murdock, Iv

Christopher A Welty

Wlodek Wlodzimierz Zadrozny

Jennifer Chu-Carroll

David C Gondek

Sugato Bagchi

Adam P Lally

Mihaela Ancuta Bornea

James William Murdock, Iv

Michael A Barborak

Alfio Massimiliano Gliozzo

Eric William Brown

Anthony Tom Levas

Liangliang Cao

James H Cross, Iii

Douglas Morgan Freimuth

Seraphin Bernard Calo

Raghu Kiran Ganti

Benjamin P Segal

John Martin Prager

Kenneth James Barker

Michael Robert Glass

Fan Ye

Pablo Ariel Duboue

Aaron K Baughman

Achille Belly Fokoue-Nkoutche

Kavitha Srinivas

Anastasios Kementsietsidis

Michael James Ward

Songyun Duan

Richard L Darden

David A Ferrucci

David Gondek

Nico M Schlaefer

Aditya A Kalyanpur

Wlodek W Zadrozny

Anthony Levas

Iv Murdock James W

Software that generates an answer to an input question using a source document by performing the following operations: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a source document; (iii) providing each dimension of each of the first vector representation and the second vector representation into a respective input node of an artificial neural network; (iv) determining whether the source document is relevant to answering the question based, at least in part, on an output generated by the artificial neural network; and (v) in response to determining that the source document is relevant, generating an answer to the question utilizing the source document.

Utilizing word embeddings for term matching in question answering systems

A mechanism is provided in a data processing system for exploring knowledge. The mechanism receives a set of known facts. The mechanism traverses paths in an ontology for a domain of knowledge from known facts in the set of known facts to one or more hypotheses. The ontology includes a plurality of entity types and a plurality of relationships between the entity types. The mechanism presents one or more hypotheses to a user.

Leveraging an external ontology for graph expansion in inference systems

A system, method and computer program product for conducting questions and answers with deferred type evaluation based on any corpus of data. The method includes processing a query including waiting until a 'Type' (i.e. a descriptor) is determined AND a candidate answer is provided. Then, a search is conducted to look (search) for evidence that the candidate answer has the required Lexical Answer Type (e.g., as determined by a matching function that can leverage a parser, a semantic interpreter and/or a simple pattern matcher). Prior to or during candidate answer evaluation, a process is provided for extracting and storing collections of entity-type pairs from semi-structured text documents. During QA processing and candidate answer scoring, a process is implemented to match the query LAT against the lexical type of each provided candidate answer and generate a score judging a degree of match.

Providing question and answers with deferred type evaluation using text with limited structure

Software that compares vector representations of question terms and passage terms in question answering systems by performing the following steps: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text; (iii) generating a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and (iv) determining whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

A common infrastructure collects data from a plurality of mobile devices and traditional sensors at Internet scale to respond to natural language queries received at different applications. The infrastructure includes a semantic interpreter to translate the natural language query to a data request specification that is processed by the data collection system. The data collection system includes a phenomenon layer that expresses data and information needs in a declarative fashion and coordinates data collection and processing for queries. An edge layer manages devices, receives collection requirements from the backend layer, configures and instructs devices for data collection, and conducts aggregation and primitive processing of data. This layer contains network edge nodes, such as base stations in a cellular network. Each node manages a set of local data generating networked devices. The device agent data layer using common agents on the networked devices receives data collection instructions and performs data collection.

QA based on context aware, real-time information from mobile devices

A system and method for automatically mapping LATs and candidate answers to multiple taxonomies without a need to merge these taxonomies. The method includes using a syntactic analysis of a corpus to extract all type instances of the LAT. The extracted instances are then mapped to a given taxonomy and clustered in a set of supertypes. Each supertype receives a score based on the coverage of LAT instances in the corpus. The method includes mapping the candidate answer to the same taxonomy to determine if the candidate answer is an instance of a significant supertype. Then the score of a candidate answer is obtained by aggregating or taking a maximum of the score of the matched significant supertypes. This score evaluates the type match between the LAT and candidate answer for a taxonomy. Multiple taxonomies can be used to increase the chance of LAT and candidate answer mapping.

Type evaluation in a question-answering system

A system, program product, and methodology automatically scores candidate answers to questions in a question and answer system. In the candidate answer scoring method, a processor device performs one or more of receiving one or more candidate answers associated with a query string, the candidates obtained from a data source having semi-structured content; identifying one or more documents with semi-structured content from the data source having a candidate answer; and for each identified document: extracting one or more entity structures embedded in the identified document; determining a number of the entity structures in the identified document that appear in the received input query; and, computing a score for a candidate answer in the document as a function of the number Overall system efficiency is improved by giving the correct candidate answers higher scores through leveraging context-dependent structural information such as links to other documents and embedded tags.

Scoring candidates using structural information in semi-structured documents for question answering systems

In a method of answering questions, a question is received, a question LAT is determined, and a candidate answer to the question is identified. Preliminary types for the candidate answer are determined using first components to produce the preliminary types. Each of the first components produces a preliminary type using different methods. A first type-score representing a degree of match between the preliminary type and the question LAT is produced. Each preliminary type and each first type-score is evaluated using second components. Each of the second components produces a second score based on a combination of the first type-score and a measure of degree that the preliminary type matches the question LAT. The second components use different methods to produce the second score. A final score representing a degree of confidence that the candidate answer matches the question LAT is calculated based on the second score.

Combining different type coercion components for deferred type evaluation

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.

Evaluating passages in a question answering computer system

Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

Semi-supervised learning of word embeddings

A computer program that generates a vector representation of a set of natural language text in a natural language processing system by: (i) receiving a first set of natural language text and a set of information pertaining to the first set of natural language text, where the information includes a dependency parse tree including a root node and a plurality of nodes that depend from the root node, where the root node represents the first set of natural language text, and where the plurality of nodes that depend from the root node represent context features of the first set of natural language text; and (ii) generating, by the natural language processing system, a first vector representation of the first set of natural language text, wherein the generating includes adding vector representations for the context features represented by the plurality of nodes that depend from the root node.

Generating distributed word embeddings using structured information

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James J Fan