no code implementations • journal 2023 • Rehan Raza a, B, Fatima Zulfiqar c, D, Muhammad Owais Khan b, C, Muhammad Arif e, Atif Alvi b, Muhammad Aksam Iftikhar c, Tanvir Alam e, *
Lung-EffNet is built based on the architecture of EfficientNet and further modified by adding top layers in the classification head of the model.
no code implementations • ACL 2020 • Simran Khanuja, D, S apat, ipan, Anirudh Srinivasan, Sunayana Sitaram, Monojit Choudhury
We present results on all these tasks using cross-lingual word embedding models and multilingual models.
no code implementations • LREC 2020 • Anirudh Srinivasan, D, S apat, ipan, Monojit Choudhury
In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees.
1 code implementation • IJCNLP 2019 • Sebastin Santy, D, S apat, ipan, Monojit Choudhury, Kalika Bali
In this paper, we demonstrate an Interactive Machine Translation interface, that assists human translators with on-the-fly hints and suggestions.
1 code implementation • 2019 Open Conference of Electrical, Electronic and Information Sciences 2019 • Chmieliauskas, D., Gursnys, D
Recent popularity grow of predictive analysis is growing in many fields.
no code implementations • COLING 2018 • Vivek Kulkarni, Yingtao Tian, D, Parth iwala, Steve Skiena
We present domain independent models to date documents based only on neologism usage patterns.
no code implementations • ACL 2018 • Raksha Sharma, Pushpak Bhattacharyya, D, S apat, ipan, Himanshu Sharad Bhatt
In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification.
no code implementations • ACL 2018 • Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, D, S apat, ipan, Kalika Bali
Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language.
Automatic Speech Recognition (ASR) Language Identification +3
no code implementations • Electrochimica Acta 2018 • Bei Ao a, Yanan Wei a, Min Wang a, Yixiao Cai a, D, *, Keryn Lian c, Jinli Qiao a, B, **
The aim of the present work is to demonstrate a novel alkaline anion-exchange membrane composed of chitosan (CS) and poly (acrylamid-co-diallyldimethylammonium chloride) (PAADDA) for electrical double layer capacitors (EDLCs).
no code implementations • IJCNLP 2017 • Pruthwik Mishra, D, Prathyusha a, Silpa Kanneganti, Soujanya Lanka
In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task.
no code implementations • IJCNLP 2017 • D, Prathyusha a, Pruthwik Mishra, Silpa Kanneganti, Soujanya Lanka
In this paper, we describe our approach to this problem and the results on four languages, i. e. English, French, Japanese and Spanish.
no code implementations • WS 2016 • D, Bharath ala, Murthy Devarakonda, Mihaela Bornea, Christopher Nielson
In predicting positive associations, the stacked combination significantly outperformed the baseline (a distant semi-supervised method on large medical text), achieving F scores of 0. 75 versus 0. 55 on the pairs seen in the patient records, and F scores of 0. 69 and 0. 35 on unique pairs.
no code implementations • WS 2016 • Shourya Roy, D, S apat, ipan, Y. Narahari
We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem.
no code implementations • LREC 2014 • D, S apat, ipan, Declan Groves
State-of-the-art statistical machine translation (SMT) technique requires a good quality parallel data to build a translation model.