Search Results for author: Rupsa Saha

Found 10 papers, 2 papers with code

Efficient Data Fusion using the Tsetlin Machine

no code implementations26 Oct 2023 Rupsa Saha, Vladimir I. Zadorozhny, Ole-Christoffer Granmo

We propose a novel way of assessing and fusing noisy dynamic data using a Tsetlin Machine.

A Relational Tsetlin Machine with Applications to Natural Language Understanding

5 code implementations22 Feb 2021 Rupsa Saha, Ole-Christoffer Granmo, Vladimir I. Zadorozhny, Morten Goodwin

TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns.

Natural Language Understanding Question Answering

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

2 code implementations10 Sep 2020 K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav

We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.

Leveraging Web Based Evidence Gathering for Drug Information Identification from Tweets

no code implementations WS 2018 Rupsa Saha, Abir Naskar, Tirthankar Dasgupta, Lipika Dey

Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78. 5{\%}, 88{\%} and 82. 9{\%} respectively for Task1 and 33. 2{\%}, 54. 7{\%} and 41. 3{\%} for Task3.

Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

no code implementations WS 2018 Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha

In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task.

Sentence Sentence Embeddings

Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

no code implementations WS 2018 Tirthankar Dasgupta, Rupsa Saha, Lipika Dey, Abir Naskar

In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text.

Clustering Feature Engineering +1

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