International Business Machines Corporation

Le Zhang

Zhe Zhang

Kyle Croutwater

Vikrant Verma

Michael D Whitley

John A Riendeau

Erinc Merdivan

Jadran Sirotkovic

In an approach to generating and using a sentence model for answer generation, one or more computer processors ingest a first corpus of a plurality of text sentences. One or more computer processors convert the plurality of text sentences into a plurality of sentence vectors. One or more computer processors group the plurality of sentence vectors into a plurality of sentence clusters, wherein a sentence cluster is composed of sentences that are semantically similar. One or more computer processors receive a second corpus. One or more computer processors determine, for each sentence cluster of the plurality of sentence clusters, a frequency each sentence cluster appears in the second corpus. Based on the determined frequency, one or more computer processors calculate a probability of each sentence cluster of the plurality of sentence clusters. Based on the calculated probabilities, one or more computer processors generate a first sentence model.

Generating and using a sentence model for answer generation

An approach is provided that receives a question at a question-answering (QA) system. A number of passages are identified that are relevant to the received question. A question knowledge graph is generated that corresponds to the question and a set of passage knowledge graphs are also generated with each passage knowledge graph corresponding to one of the identified passages. Each of the passage knowledge graphs are compared to the question knowledge graph with the comparison resulting in a set of knowledge graph candidate answers (kgCAs). A set of candidate answers (CAs) is computed by the QA with at least one of the CAs being based on one of the kgCAs.

Leveraging entity relations to discover answers using a knowledge graph

Embodiments are directed to a system, a computer program product, and a method for identification of linguistically related elements, and more specifically to prediction of a linguistically related element. A linguistic algorithm forms a cluster representation of corpus entries. A linguistic term is identified and applied to the cluster representation to identify proximally related linguistic terms. Associative relationships between the proximally related terms and category metadata are iteratively investigated. One or more linguistic terms related across the two more metadata categories is identified and designated as the linguistically related element.

Dynamic linguistic assessment and measurement

A computer-implemented method of executing a virtual agent bot includes receiving, via a computer server, at least one input query from a user, and analyzing the at least one input query to extract at least one input term. The method further comprises determining a semantic skill set of the virtual agent bot among a plurality of different candidate skill sets based at least in part on the at least one input term; and invoking the virtual agent bot to provide a semantic topic response corresponding to the semantic skill set to provide an answer to the at least one input query.

Computing system including virtual agent bot providing semantic topic model-based response

An approach is provided that receives a question at a question answering (QA) system. The question includes a number of words. The approach operates by calculating weights that correspond to search terms included in the plurality of words. The search terms include the plurality of words and may include terms that are one or more sequences of adjacent words included in the question. Based on the calculated weights and the words in the question, the approach generates a query that is used to search a corpus that is managed by the QA system with the search resulting in one or more search results.

Weighting and expanding query terms based on language model favoring surprising words

An approach is provided in which a knowledge manager identifies a first cohort type and a second cohort type corresponding to an entity included in a question. The knowledge manager determines inferred states to the question by comparing a first set of cohort attributes corresponding to a first cohort type against entity attributes corresponding to the question. In turn, the knowledge manager generates possible answers by comparing the inferred states against a second set of cohort attributes corresponding to a second cohort type.

Inferred facts discovered through knowledge graph derived contextual overlays

Mechanisms for processing a knowledge canvassing request receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Knowledge canvassing using a knowledge graph and a question and answer system

A computer-implemented method generates a candidate answer triple for use in retrieving information used to answer a question. One or more processors parse a question to identify a lexical answer type for the question, a question action for the question, and a question timestamp for the question to make up a question triple. One or more processors retrieve multiple candidate passages for answering the question, and parse each of the multiple candidate passages to identify a candidate entity, a candidate action, and a candidate timestamp from each of the multiple candidate passages to generate a candidate answer triple. One or more processors compare the question triple to the candidate answer triple and establish a match score for each candidate answer triple, which is used in retrieving information used to answer the question.

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Durham, NC, United States of America

Le Zhang