Tata Consultancy Services Limited

Swapnil Vishveshwar Hingmire

Girish Keshav Palshikar

Sachin Sharad Pawar

Rajiv Radheyshyam Srivastava

Sangameshwar Suryakant Patil

Vasudeva Varma Kalidindi

Pushpak Bhattacharyya

Manoj Madhav Apte

Abhay Sodani

Savita Suhas Bhat

Pragati Hiralal Dungarwal

Saheb Chourasia

Ravina Vinayak More

This disclosure relates generally to extraction of cause-effect relation from domain specific text. Cause-effect relation highlights causal relationship among various entities, concepts and processes in a domain specific text. Conventional state-of-the-art methods use named entity recognition for extraction of cause-effect (CE) relation which does not give precise results. Embodiments of the present disclosure provide a knowledge-based approach for automatic extraction of CE relations from domain specific text. The present disclosure method is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. The method extracts the CE relation in the form of a triplet comprising a causal trigger, a cause phrase and an effect phrase identified from the domain specific text. The disclosed method is used for extracting CE relations in biomedical text.

Method and system for extraction of cause-effect relation from domain specific text

The present disclosure provides a system and method for recommending context and sequence aware based training set to a user. The system identifies various items and keywords of a plurality of earlier trainings of the users' interest and generates a context and sequence aware recommendation model based on the context of the identified keywords. It uses a collapsed Gibbs Sampling as in generative modelling for prior trainings. Further, it applies the context and sequence aware recommendation model on various keywords that are of users' interest. The context and sequence aware recommendation model infers a plurality of subsequent trainings based on context derived from the keywords. In addition to this, the model is generated to rank the inferred plurality of subsequent topics using a probability distribution over subsequent keywords. At the last, it recommends at least one topic to the user based on ranking of the plurality of trainings.

System and method for context and sequence aware recommendation

The present invention relates to system and method for evaluating reviewer's ability to provide feedback. The system receives feedback given by the reviewer that includes qualitative feedback and quantitative feedback. The system performs scoring of qualitative feedback to evaluate level of noise, suggestions, appreciation, specificity and duplicate comments in the qualitative feedback. Further, the system performs scoring of quantitative feedback that includes realistic score, softness score and critical nature score. Subsequently, the scores of qualitative feedback and quantitative feedback are aggregated to provide a rank to the reviewer for the reviewer's ability to provide feedback.

System and method for evaluating reviewer's ability to provide feedback

The present subject matter relates to method(s) and system(s) to rank human profiles based on selection criteria personalized to a selector. In an embodiment, the method includes obtaining querying criteria from the selector to query a database comprising a set of human profiles. Further, a subset of human profiles is determined from the set of human profiles based on the querying criteria and a default ranking mechanism. Furthermore, a selection based ranking is obtained for the subset of human profiles. Further, based on the selection based ranking, a ranking function is determined that is indicative of a relative inclination of the selector towards the one or more implicit attributes. Such a determination is by capturing at least one implicit attribute in the ranking function from the selection based ranking. Further, the ranking function is applied to rank a fresh set of human profiles based on the ranking function.

Methods and systems for ranking of human profiles

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Pune, India

Swapnil Vishveshwar Hingmire