Search Results for author: Subba Reddy Oota

Found 18 papers, 3 papers with code

Multi-view and Cross-view Brain Decoding

no code implementations COLING 2022 Subba Reddy Oota, Jashn Arora, Manish Gupta, Raju S. Bapi

(2) Our extensive analysis across 9 broad regions, 11 language sub-regions and 16 visual sub-regions of the brain help us localize, for the first time, the parts of the brain involved in cross-view tasks like image captioning, image tagging, sentence formation and keyword extraction.

Brain Decoding Image Captioning +2

TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers

no code implementations ACL 2022 Suma Reddy Duggenpudi, Subba Reddy Oota, Mounika Marreddy, Radhika Mamidi

Our contributions in this paper include (i) Two annotated NER datasets for the Telugu language in multiple domains: Newswire Dataset (ND) and Medical Dataset (MD), and we combined ND and MD to form Combined Dataset (CD) (ii) Comparison of the finetuned Telugu pretrained transformer models (BERT-Te, RoBERTa-Te, and ELECTRA-Te) with other baseline models (CRF, LSTM-CRF, and BiLSTM-CRF) (iii) Further investigation of the performance of Telugu pretrained transformer models against the multilingual models mBERT, XLM-R, and IndicBERT.

named-entity-recognition Named Entity Recognition +2

Speech language models lack important brain-relevant semantics

no code implementations8 Nov 2023 Subba Reddy Oota, Emin Çelik, Fatma Deniz, Mariya Toneva

We investigate this question via a direct approach, in which we eliminate information related to specific low-level stimulus features (textual, speech, and visual) in the language model representations, and observe how this intervention affects the alignment with fMRI brain recordings acquired while participants read versus listened to the same naturalistic stories.

Language Modelling

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

no code implementations17 Jul 2023 Subba Reddy Oota, Manish Gupta, Raju S. Bapi, Gael Jobard, Frederic Alexandre, Xavier Hinaut

In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets.

Syntactic Structure Processing in the Brain while Listening

no code implementations16 Feb 2023 Subba Reddy Oota, Mounika Marreddy, Manish Gupta, Bapi Raju Surampud

In this study, we investigate the predictive power of the brain encoding models in three settings: (i) individual performance of the constituency and dependency syntactic parsing based embedding methods, (ii) efficacy of these syntactic parsing based embedding methods when controlling for basic syntactic signals, (iii) relative effectiveness of each of the syntactic embedding methods when controlling for the other.

Activity Prediction Dependency Parsing +1

GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages

1 code implementation25 Dec 2022 Lakshmi Sireesha Vakada, Anudeep Ch, Mounika Marreddy, Subba Reddy Oota, Radhika Mamidi

Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages.

Document Summarization

Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language

1 code implementation2 May 2022 Mounika Marreddy, Subba Reddy Oota, Lakshmi Sireesha Vakada, Venkata Charan Chinni, Radhika Mamidi

Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc.

Graph Reconstruction Sarcasm Detection +6

Visio-Linguistic Brain Encoding

no code implementations COLING 2022 Subba Reddy Oota, Jashn Arora, Vijay Rowtula, Manish Gupta, Raju S. Bapi

In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding.

Cross-view Brain Decoding

no code implementations18 Apr 2022 Subba Reddy Oota, Jashn Arora, Manish Gupta, Raju S. Bapi

Also, the decoded representations are sufficiently detailed to enable high accuracy for cross-view-translation tasks with following pairwise accuracy: IC (78. 0), IT (83. 0), KE (83. 7) and SF (74. 5).

Brain Decoding Image Captioning +4

Wound and episode level readmission risk or weeks to readmit: Why do patients get readmitted? How long does it take for a patient to get readmitted?

no code implementations5 Oct 2020 Subba Reddy Oota, Nafisur Rahman, Shahid Saleem Mohammed, Jeffrey Galitz, Ming Liu

On a combined wound & episode-level data set of patient's wound care information, our extended autoprognosis achieves a recall of 92 and a precision of 92 for the predicting a patient's re-admission risk.

Management

Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts

no code implementations26 Sep 2019 Subba Reddy Oota, Naresh Manwani, Raju S. Bapi

In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model.

Clustering regression

Affect in Tweets Using Experts Model

no code implementations PACLIC 2018 Subba Reddy Oota, Adithya Avvaru, Mounika Marreddy, Radhika Mamidi

We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT).

Sentiment Analysis

Mixture of Regression Experts in fMRI Encoding

no code implementations26 Nov 2018 Subba Reddy Oota, Adithya Avvaru, Naresh Manwani, Raju S. Bapi

We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model.

regression

fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings

no code implementations13 Jun 2018 Subba Reddy Oota, Naresh Manwani, Bapi Raju S

Unlike the models with hand-crafted features that have been used in the literature, in this paper we propose a novel approach wherein decoding models are built with features extracted from popular linguistic encodings of Word2Vec, GloVe, Meta-Embeddings in conjunction with the empirical fMRI data associated with viewing several dozen concrete nouns.

Word Embeddings

Clickbait detection using word embeddings

no code implementations8 Oct 2017 Vijayasaradhi Indurthi, Subba Reddy Oota

Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching thumbnail pictures to attract click-throughs and to encourage forwarding of the material over online social networks.

Clickbait Detection Feature Engineering +2

Cannot find the paper you are looking for? You can Submit a new open access paper.