Tata Consultancy Services Limited

Sangameshwar Suryakant Patil

Girish Keshav Palshikar

Indrajit Bhattacharya

Sachin Sharad Pawar

Vasudeva Varma Kalidindi

Soham Datta

Harsimran Bedi

Prabir Mallick

Rajiv Radheyshyam Srivastava

Pushpak Bhattacharyya

Swapnil Vishveshwar Hingmire

Abhay Sodani

Nitin Vijaykumar Ramrakhiyani

Tapas Nayak

Savita Suhas Bhat

Samiran Pal

Avinash Kumar Singh

Yash Agrawal

Apoorv Shrivastava

Anumita Dasguptabandyopadhyay

Swapnil Vishweshwar Hingmire

Pragati Hiralal Dungarwal

The method and system of the present disclosure facilitate automatic, and in an objective manner, selection of an optimal set of technical questions, from a question bank, personalized for a candidate. This ensures consistent, standardized, efficient and objective interviews that result in high quality recruitment, given the diversity of candidates, complexity of job requirements and interviews are inherently subjective. The state-of-the-art depends on responses so far, to generate on-the-fly questions causing a cognitive load on the interviewer. Also, there is no guarantee on the breadth and depth of concepts assessed in each interview. In the present disclosure, skill graphs are employed to create a semantically rich and detailed characterization of questions in terms of concepts. Optimization formulation uses the skill graph to generate constraints, content balancing and objective functions for selection of questions.

Integer linear programming based generation of a personalized optimal questionnaire using a skill graph

Questions play a central role in assessment of a candidate's expertise during an interview or examination. However, generating such questions from input text documents manually needs specialized expertise and experience. Further, techniques that are available for automated question generation require input sentence as well as an answer phrase in that sentence to generate question. This in-turn requires large training datasets consisting tuples of input sentence answer-phrase and the corresponding question. Additionally, training datasets are available are for general purpose text, but not for technical text. Present application provides systems and methods for generating technical questions from technical documents. The system extracts meta information and linguistic information of text data present in technical documents. The system then identifies relationships that exist in provided text data. The system further creates one or more graphs based on the identified relationships. The created graphs are the used by the system to generate technical questions.

Automated systems and methods for generating technical questions from technical documents

This disclosure relates generally to long-form answer extraction and, more particularly, to long-form answer extraction based on combination of sentence index generation techniques. Existing answer extractions techniques have achieved significant progress for extractive short answers; however, less progress has been made for long form questions that require explanations. Further the state-of-art long-answer extractions techniques result in poorer long-form answers or not address sparsity which becomes an issue longer contexts. Additionally, pre-trained generative sequence-to-sequence models are gaining popularity for factoid answer extraction tasks. Hence the disclosure proposes a long-form answer extraction based on several steps including training a set of generative sequence-to-sequence models comprising a sentence indices generation model and a sentence index spans generation. The trained set of generative sequence-to-sequence models is further utilized for model long-form answer extraction based on a union of several sentence index generation techniques comprising a sentence indices and a sentence index spans.

Method and system for long-form answer extraction based on combination of sentence index generation techniques

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

Existing software defect text categorization approaches are based on use of supervised/semi-supervised machine learning techniques, which may require significant amount of labeled training data for each class in order to train the classifier model leading to significant amount of human effort, resulting in an expensive process. Embodiments of the present disclosure provide systems and methods for circumventing the problem of dependency on labeled training data and features derived from source code by performing concept based classification of software defect reports. In the present disclosure, semantic similarity between the defect category/type labels and the software defect report(s) is computed and represented in a concept space spanned by corpus of documents obtained from one or more knowledge bases, and distribution of similarity values are obtained. These similarity values are compared with a dynamically generated threshold, and based on the comparison, the software defect reports are classified into software defect categories.

Systems and methods for classification of software defect reports

The present subject matter discloses system and method for tagging set of text snippets with set of tags. A set of text snippet and set of tags are received as input by the system. Further, each tag comprises set of words, and for each word of the set of words, numeric weight is assigned based on frequency of the word and headword of the set of words. Further, same words and similar meaning words are determined from the tag and text snippets. Further, belief factor is computed for the tag by applying certainty factor algebra upon the numeric weight assigned to the same words and the similar meaning words. Further, the tag is assigned to the text snippet based on comparison of the belief factor with threshold. Further, feedback is received about the tagging done. Based on the feedback, knowledge base of the system may be updated for future tagging.

Tagging text snippets

Disclosed is a system for rectifying a typographical error in a text file. The system includes a network generating module for generating a linguistic network of a plurality of words present in the text file. A computation module configured to compute the similarity between each pair of words based on a set of parameters. A weight assignment module for assigning a weight to the edge present between the each pair of words based the set of parameters. A categorization module configured to categorize one or more words present in the linguistic network in a category. A word identification module configured to identify a reference word from the category. A word substitution module configured to substitute each word of the category deemed as erroneous with corresponding reference word for rectifying the typographical error.

System and method for rectifying a typographical error in a text file

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.

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Pune, India

Sangameshwar Suryakant Patil