Tata Consultancy Services Limited

Mohammad Shakir

Lipika Dey

Ishan Verma

Tirthankar Dasgupta

Tushar Goel

Abir Naskar

Gargi Roy

Saumya Bhadani

Aninda Sukla

Suyash Sangwan

Akshara Rai

Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined. A customized causal insight summary is delivered to target users based on user preferences associated with specific named entities and impacting events.

Systems and methods for generating causal insight summary

State of the art systems used for text processing fail to differentiate between domain terms and noise terms that form a major part of enterprise and social media data. The disclosure herein generally relates to text processing, and, more particularly, to segregating domain words and noise terms from the text. The system processes input data and classifies words as dictionary words and non-dictionary words. The system processes the dictionary words and non-dictionary words and identifies domain terms and noise terms. The system then corrects the words identified as the noise terms.

Method and system for text cleaning

With the proliferation of data and documents available on the internet and other information sources, analysis of adverse events poses a serious technical challenge on account of associated data volume and variety. This disclosure relates generally to identification and profiling of adverse events. By receiving a set of articles from a plurality of data sources and utilizing a series of Natural Language Processors, NLP techniques are employed to identify implicit and explicit adverse events. Entity statistics and sentiment extraction and analysis is performed. An ontology based adverse event identification framework is proposed for identification and profiling of implicit adverse event. An attention based bi-directional long short term memory network for adverse event identification and classification is proposed.

System and method for identification and profiling adverse events

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Gurgaon, India

Mohammad Shakir