Tata Consultancy Services Limited

Sachin Sharad Pawar

Girish Keshav Palshikar

Vasudeva Varma Kalidindi

Pushpak Bhattacharyya

Rajiv Radheyshyam Srivastava

Swapnil Vishveshwar Hingmire

Manoj Madhav Apte

Nitin Vijaykumar Ramrakhiyani

Sangameshwar Suryakant Patil

Anindita Sinha Banerjee

Rahul Ramesh Kelkar

Harrick Mayank Vin

Amrish Shashikant Pathak

Sandeep Chougule

Abhay Sodani

Harsimran Bedi

Savita Suhas Bhat

Sharmishtha Prakash Kulkarni

Sangemeshwar Suryakant Patil

Sriram Baskaran

Amol Madhukar Aaeer

Swapnil Vishweshwar Hingmire

Deepak Pandita

Anil Kumar Kurmi

Pragati Hiralal Dungarwal

Swapnil Vishbeshwar Hingmire

Kripabandhu Ghosh

Devavrat Shailesh Thosar

Ravina Vinayak More

Akshada Ananda Shinde

Aditi Anil Pawde

Sushodhan Sudhir Vaishampayan

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

This disclosure relates to extraction of tasks from documents based on a weakly supervised classification technique, wherein extraction of tasks is identification of mentions of tasks in a document. There are several prior arts addressing the problem of extraction of events, however due to crucial distinctions between events-tasks, task extraction stands as a separate problem. The disclosure explicitly defines specific characteristics of tasks, creates labelled data at a word-level based on a plurality of linguistic rules to train a word-level weakly supervised model for task extraction. The labelled data is created based on the plurality of linguistic rules for a non-negation aspect, a volitionality aspect, an expertise aspect and a plurality of generic aspects. Further the disclosure also includes a phrase expansion technique to capture the complete meaning expressed by the task instead of merely mentioning the task that may not capture the entire meaning of the sentence.

Extraction of tasks from documents using weakly supervision

Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, present disclosure provides system and method that address the task of generating audit recommendations that can help auditors to focus their investigations. Adverse remarks, financial variables mentioned in each sentence are extracted/identified from audit reports and category tag is assigned accordingly, thus creating a knowledge base for generating audit recommendations using a trained sentence classifier. In absence of labeled data, the system applies linguistic rule(s) to identify adverse remark sentences, and automatically create labeled training data for training the sentence classifier. For a given financial statement and financial variables in the audit report that contribute to suspiciousness, the system compares these with the extracted knowledge base and identify aligned adverse remarks that help auditor(s) in focusing on specific directions for further investigations.

Identifying and categorizing adverse remarks from audit reports for knowledge base creation and generating recommendations

Extracting data from documents is challenging due to the variation in structure, content, styles across geographies and functional areas. Further complex relation types are characterized by one or more of N-ary entity mention arguments, cross sentence span of entity mentions for a relation mention, missing entity mention arguments and entity mention arguments being multi-valued. The present disclosure addresses these gaps in the art to extract entity mentions and relation mentions using a joint neural network model including two sequence labelling layers which are trained jointly. The mentions are extracted from documents to facilitate downstream processing. A first RNN layer creates sentence embeddings for each sentence in the document being processed and predicts entity mentions. A second RNN layer predicts labels for each sentence span corresponding to a relation type. Using predefined rules, entity mentions for each entity type are selected for extracting relation mentions from the identified sentence spans.

Extracting mentions of complex relation types from documents by using joint first and second RNN layers to determine sentence spans which correspond to relation mentions

This disclosure relates generally to retrieval of prior court cases using witness testimonies. Conventional state-of-the-art methods use supervised techniques for answering basic questions in legal domain using numerous features and do not address interpretability of results and the performance and precision of retrieving prior court cases for these methods are less. Embodiments of the present disclosure obtains an embedded representation for an event structure of a user query and testimony sentences identified from prior court cases using a trained Bi-LSTM classifier and a set of linguistic rules. A similarity is estimated between the embedded representation for the event structure of the user query and the event structure of each testimony sentence from the prior court cases. Further a relevance score is assigned in accordance with the estimated similarity to retrieve the relevant prior court cases. The disclosed method is used to retrieve the relevant prior court cases using witness testimonies.

Method and system for retrieval of prior court cases using witness testimonies

Narrative texts contain rich knowledge about actors and interactions among them. It is often useful to extract and visualize these interactions through a set of inter-related timelines in which an actor has participated. Current approaches utilize labeled datasets and implement supervised techniques and thus are not suitable. Embodiments of the present disclosure implement systems and methods for automated extraction of Message Sequence Chart (MSC) from textual description by identifying verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. The present disclosure further employs an optimization-based approach to temporally re-order these interactions.

Automated extraction of message sequence chart from textual description

Event prediction system and method includes gathering data corresponding to multiple entities to derive multiple entity profiles. Next, a first subset of entity profiles is identified from the multiple entity profiles generated. The identification is done on the basis of characteristics associated with the entities. Subsequent to identification of the first subset of the entity profiles, a second subset of entity profiles is shortlisted. Here, the second subset of entity profiles shows highest probability of occurrence of the event. Further, a determination of a factor that may lead to occurrence of the event is done.

Event prediction system and method

System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.

Method and system for entity relationship model generation

Text analysis, specifically, narratives, wherein identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. Identifying aliases of participants in a narrative is crucial for NLP applications. Existing conventional methods are supervised for alias identification which requires a large amount of manually annotated (labeled) data and are also prone to errors. Embodiments of the present disclosure provide systems and methods that implement Markov Logic Network (MLN) to encode linguistic knowledge into rules for identification of aliases for aliases mention identification using proper nouns, pronouns or noun phrases with common noun headword.

Markov logic networks based alias links identification and canonical mention selection in text

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

This disclosure relates generally to performance appraisal management, and more particularly to standardization of goals associated with performance appraisal. In one embodiment, a method for standardization of goals includes identifying labeled and unlabeled goals associated with a role. The goals includes template and manually created goals. Each of the template goals is associated with a class label, and includes corresponding goal description and self-comments. First and second classifiers are trained using goal description and self-comments. Candidate negative goals are identified and excluded from the goals to obtain a set of unlabeled goals. The set of unlabeled goals are classified by the first and second classifier, and a confidence score associated with the classification is determined. The unlabeled goals with high confidence score are added to labeled goals to obtain an updated set of labeled goals. The first and second classifiers are iteratively co-trained using the updated set of labeled goals.

System for standardization of goal setting in performance appraisal process

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.

Growing community of inventors

Pune, India

Sachin Sharad Pawar