Tata Consultancy Services Limited

Godfrey Claudin Mathais

Pranav Champaklal Shah

Sudhakara Deva Poojary

Paras Dwivedi

Siva Prasad Pusarla

Varsha Nayak

Nidhi Harshad Shroff

Amit Aggrawal

This disclosure relates to systems and methods for multi-utterance generation of data. Embodiments of the present disclosure utilizes a smart framework that is capable of generating contextually relevant utterances with immutability regulation and punctuation-memory. More Specifically, the present disclosure generates multiple syntactically and semantically correct utterances for text input data in such a way that a provision to selectively maintain or regulate phrases or words intact is provided and punctuation consistency is maintained.

Systems and methods for multi-utterance generation of data with immutability regulation and punctuation-memory

Embodiments provide a system and method for statistical subject identification. The system takes texts, videos, audios, and images as input for which subject needs to be identified. The system pre-process input data and generates n-grams and pre-processed text strings by removing stopwords, punctuations, selective POS tags and lemmatization. Frequency distribution of n-grams are computed, and weightage of n-grams is assigned. For each n-gram, sum of weights across all text strings is computed and a maximum weightage is identified. The computed value as a result of taking a ratio of two, is assigned to each of the n-grams. Values computed for the n-grams have a non-normal distribution, when observed statistically. Thus, the n-gram values are transformed to confidence value following a normal distribution. The system maps the n-gram domains using a domain lexicon. Finally, these domains are aggregated and converged for subject identification based on a pre-annotated mapping dictionary.

System and method for statistical subject identification from input data

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Godfrey Claudin Mathais