International Business Machines Corporation

Mandar Mutalikdesai

Sheetal Srivastava

Anagha M

Ashish Rao

Lavina Durgani

Kartikeya Vats

Sudarsan Lakshminarayanan

Sandipto Neogi

Veerababu Moodu

Raunak Swarnkar

Shrishti Aggarwal

Pranjal Srivastava

Arjun Das

Ratnanu Ghosh-Roy

Craig M Trim

Mohit Sewak

Yash Veer Singh

Sachchidanand Singh

Ratul Sarkar

Veronica Wyatt

Shivangi Tak

Simardeep Singh Arneja

Gregory Bovee

Rahul Reddy Nimmakayala

Balachandra Deshpande

Arnika Kumar

Binoy Damodaran

Shivam Ratnakar

Debasish Kanhar

Yash Vardhan Singh

Ojasvi Bhalerao

A method and related system determine a localness measure that represents accessibility of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving focus node (FN) information related to an FN of a graph representing the signed network, receiving neighbor node (NN) information related to a plurality of NNs in the signed network, and determining the localness measure based on the NN information and the FN information. The method and system also determine a harmony measure that represents importance of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving source node (SN) information related to an SN of a graph representing the signed network, receiving destination node (DN) information related to a DN in the signed network, and determining the harmony measure based on the SN information and the DN information.

Global and local measures of centrality for signed and unsigned networks

Documents needing to be analyzed for various reasons, such as financial crimes, are ranked by examining the topicality and sentiment present in each document for a given subject of interest. In one approach a given document is classified to determine its category, and entity recognition is used to identify the subject of interest. Passages from the document that relate to the entity are grouped and analyzed for sentiment to generate a sentiment score. Documents are then ranked based on the sentiment scores. In another approach, a classification probability score is computed for each passage representing a likelihood that the passage relates to a category of interest, and the document is ranked based on the sentiment scores and the classification probability scores. The category classification uses an ensemble of natural language text classifiers. One of the classifiers is a na&#xef;ve Bayes classifier with feature vectors generated using Word2Vec modeling.

Contextual interestingness ranking of documents for due diligence in the banking industry with entity grouping

One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.

Unsupervised contextual label propagation and scoring

An approach is provided in which the approach applies, by a first node, a first axiom to a set of data points to generate a set of first outputs. The approach applies, by a second node, a second axiom to the set of data points to generate a set of second outputs. The first node and the second node are part of a computer network that includes multiple nodes. The approach computes a first nuance based on a set of disagreements between the set of first outputs and the set of second outputs, and adjusts a reliability of the first node in the computer network based on the first nuance.

Preserving data integrity in cognitive multi-agent systems

Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.

Learning roadmaps from unstructured text

A mixing pattern system for networks is provided. One or more nodes in a network are analyzed. Grouping the one or more nodes into one or more classes within the network. A computer device analyzes one or more transactions between the one or more nodes in the network that include nodes within similar or distinct classes of the one or more nodes. A computer device identifies one or more mixing patterns associated with one or more transactions between the one or more nodes.

Network mixing patterns

An approach for enhancing user communication with a chatbot is provided. In an embodiment, a communication from a user using a chatbot application is received. One or more intents related to the user communication are identified, with each intent having a respective confidence score. Further, any number of attributes related to the user communication are extracted. If multiple viable intents are found (e.g., intents having a confidence score exceeding a predefined threshold), then relationships between the extracted attributes are derived to establish a context. Based on the context, a disambiguation question is generated to clarify an actual intent of the user.

Enhanced conversational bots processing

Contextual interestingness ranking of documents for due diligence in the banking industry with topicality grouping

Systems and methods for generating search results based on non-linguistic tokens are disclosed. In embodiments, a computer-implemented method comprises: mapping, by a computing device, a non-linguistic token to documents during indexing based on associations in a token database between the non-linguistic token and variations of a multi-word term; receiving, by the computing device, the multi-word term in a search query; determining, by the computing device, the non-linguistic token associated with the multi-word term using the token database; and generating, by the computing device, search results based on the multi-word term and the non-linguistic token.

Generating search results based on non-linguistic tokens

Sentiment analysis is targeted toward a specific subject of interest (or selected subjects) in a passage of natural language text. A dependency tree is generated for the passage, and subtrees are found that have sentiment polarities which contribute to the subject(s) of interest. A targeted sentiment score is computed for the subject(s) of interest based on sentiment expressed in those subtrees. Consecutively occurring nouns in the passage are collapsed into a noun phrase, as are possessives with ensuing nouns. The sentiment expressed in a given subtree can be modified using various linguistic heuristics. For example, sentiment polarity which is modified by a negation word may be inverted, sentiment polarity which is modified by an intensifying word may be increased, or sentiment polarity which is modified by a diluting word may be decreased.

Unsupervised targeted sentiment analysis using dependency parsing and linguistic heuristics

Unsupervised sentiment analysis for multiple subjects of interest using dependency parsing and linguistic heuristics

Methods and systems for generating cognitive real-time pictorial summary scenes are disclosed. A method includes: obtaining, by a computing device, a document; training, by the computing device, computer models using natural language processing and deep learning based computer vision; and creating, by the computing device, a first pictorial summary scene that summarizes the document using the computer models.

Cognitive adaptive real-time pictorial summary scenes

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Bengaluru, India

Mandar Mutalikdesai