Tata Consultancy Services Limited

Lipika Dey

Ishan Verma

Mohammad Shakir

Gautam Shroff

Abir Naskar

Tushar Goel

Tirthankar Dasgupta

Geetika Sharma

Aditeya Pandey

Chandan Kumar Singh

Suyash Sangwan

Priyankar Ghosh

Akshara Rai

Saumya Bhadani

Indira Priyadarsini Muthukrishnan

Anjan Dutta

Somya Singh

Uma Mundoli Narayanan

Gargi Roy

Kunal Ranjan

Subramanian Kuppuswami

Rajkumar Pallikuth

Rahul Kanna Rajarathinam

Parvatharaj Sundaresan Balasubramanian

Arpit Khurdiya

Aninda Sukla

Rupsa Saha

Diwakar Mahajan

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

Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.

Method and system for sustainability measurement

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

System and method for method and system for event profiling is described that processes large volume of data gathered from a plurality of digital sources to automatically profile and continuously update an event. The system utilizes, an ensemble of probabilistic classifiers for automated extraction of finer details of the event, which use linguistic features for profiling information about the event, wherein the information is spread across various data sources. Further, disambiguation is performed to augment the accuracy of the event profiling. The system enables semantically linking of related events curated in the knowledge base and thereby performs semantic search over it. The system takes user-feedback and improves upon the information extraction process through reinforcement learning.

System and method for event profiling

A method and system is provided for analyzing temporal text data. Particularly, the present application provides a method and system for analyzing temporal text data, comprises taking temporal text data as an input from voluminous data sources; implementing text analysis on the input data for information extraction and determination of top concepts; sending the analyzed text to a shared file system for storage purpose; clustering the analyzed text as per frequency distribution of top users, concepts and tag clouds and presenting the results in the form of a visualization dashboard.

System and method for analysing temporal text data

A method for performing email analytics is described. The method includes extracting emails from the configured email repository. The emails are then grouped into mail groups based on identification of content similarity of the emails. A network graph is then constructed for each of the mail group to identify an association of emails in the mail group based on header-level analysis of emails. Thereafter, email analytics is performed on the mail groups by clustering the mail groups into mail clusters based on temporal progression of emails in the mail groups. Key phrases are then determined based on a content analysis of emails in the mail groups in the mail clusters. The key phrases are then associated with the network graphs of the mail groups.

Email analytics

The present subject matter relates to analysis of time-series data based on world events derived from unstructured content. According to one embodiment, a method comprises obtaining event information corresponding to at least one world event from unstructured content obtained from a plurality of data sources. The event information includes at least time of occurrence of the world event, time of termination of the world event, and at least one entity associated with the world event. Further, the method comprises retrieving time-series data pertaining to the entity associated with the world event from a time-series data repository. Based on the event information and the time-series data, the world event is aligned and correlated with at least one time-series event to identify at least one pattern indicative of cause-effect relationship amongst the world event and the time-series event.

Time-series analysis based on world event derived from unstructured content

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Gurgaon, India

Lipika Dey