International Business Machines Corporation

Anagha M

Mandar Mutalikdesai

Lavina Durgani

Sheetal Srivastava

Sudarsan Lakshminarayanan

Arjun Das

Ratnanu Ghosh-Roy

Veerababu Moodu

Raunak Swarnkar

Shrishti Aggarwal

Yash Veer Singh

Ashish Rao

Sandipto Neogi

Pranjal Srivastava

Shivangi Tak

Shivam Ratnakar

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

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

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

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

Growing community of inventors

Bengaluru, India

Anagha M