Browsing by Author "Meghana, S."

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    Modeling Uber Data for Predicting Features Responsible for Price Fluctuations
    (Institute of Electrical and Electronics Engineers Inc., 2022) Sindhu, P.; Gupta, D.; Meghana, S.; Anand Kumar, M.
    In the field of economics, the features and patterns of the transportation system, including classical modes of transportation such as subways and taxis, as well as innovative tools such as car pooling platforms(Uber, Lyft, etc), are key research topics. The study here demonstrates how an Uber dataset is, which comprises Uber's New York City data, works. Uber is an online service provider platform via internet or a mobile application that avails ride-hailing service. In essence, it matches passengers with drivers of vehicles to book a ride from one place to another. The service connects users with drivers who will drive them to their desired location. The dataset contains primary data about Uber pick-ups, including the date, time, longitude, and latitude coordinates. The paper attempts to examine data from different locations, weathers, hours, and dates (intraday and midweek) in New York City and apply time series data analysis, statistical regression on the dataset, and predict Uber ride prices. We arrive at conclusions by analyzing data using various graphs, calculating and estimating the influence of these elements on Uber riders' payment amounts, and emphasizing features that cause price fluctuation. © 2022 IEEE.
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    NITK_LEGAL at SemEval-2023 Task 6: A Hierarchical based system for identification of Rhetorical Roles in legal judgements
    (Association for Computational Linguistics, 2023) Sindhu, P.; Gupta, D.; Meghana, S.; Anand Kumar, M.
    The ability to automatically recognise the rhetorical roles of sentences in a legal case judgement is a crucial challenge to tackle since it can be useful for a number of activities that come later, such as summarising legal judgements and doing legal searches. The task is exigent since legal case documents typically lack structure, and their rhetorical roles could be subjective. This paper describes SemEval-2023 Task 6: LegalEval: Understanding Legal Texts, Sub-task A: Rhetorical Roles Prediction (RR). We propose a system to automatically generate rhetorical roles of all the sentences in a legal case document using Hierarchical Bi-LSTM CRF model and RoBERTa transformer. We also showcase different techniques used to manipulate dataset to generate a set of varying embeddings and train the Hierarchical Bi-LSTM CRF model to achieve better performance. Among all, model trained with the sent2vec embeddings concatenated with the handcrafted features perform better with the micro f1-score of 0.74 on test data. The dataset utilised in our task is available at 1 © 2023 Association for Computational Linguistics.