no code implementations • 30 Aug 2023 • Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, William Marshall, Gurpreet Gosal, Cynthia Liu, Zhiming Chen, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, Lalit Pradhan, Zain Muhammad Mujahid, Massa Baali, Xudong Han, Sondos Mahmoud Bsharat, Alham Fikri Aji, Zhiqiang Shen, Zhengzhong Liu, Natalia Vassilieva, Joel Hestness, Andy Hock, Andrew Feldman, Jonathan Lee, Andrew Jackson, Hector Xuguang Ren, Preslav Nakov, Timothy Baldwin, Eric Xing
We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs.
no code implementations • 2 Aug 2020 • Sunil Kumar Sahu, Derek Thomas, Billy Chiu, Neha Sengupta, Mohammady Mahdy
The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs.
1 code implementation • ACL 2020 • Billy Chiu, Sunil Kumar Sahu, Derek Thomas, Neha Sengupta, Mohammady Mahdy
The graph is constructed with topical keywords as nodes and multiple local and global features as edges.
no code implementations • ACL 2019 • Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.
no code implementations • 22 Jan 2018 • Ameya Godbole, Aman Dalmia, Sunil Kumar Sahu
We got the best result by using the Siamese adaptation of a Bidirectional GRU with a Random Forest classifier, which landed us among the top 24% in the competition Quora Question Pairs hosted on Kaggle.
no code implementations • WS 2017 • Kushal Chawla, Sunil Kumar Sahu, Ashish Anand
Our experiments focus on important contextual words as features, which can easily be extended to analyze various other feature types.
no code implementations • 11 Aug 2017 • Sunil Kumar Sahu, Ashish Anand
We systematically investigate effectiveness of the proposed frameworks in transferring the knowledge under multiple aspects related to source and target tasks, such as, similarity or relatedness between source and target tasks, and size of training data for source task.
no code implementations • 11 Aug 2017 • Sunil Kumar Sahu, Ashish Anand
Three important characteristics of the framework are as follows - (1) model learns contextual as well as morphological features using two different BLSTM in hierarchy, (2) model uses first order linear conditional random field (CRF) in its output layer in cascade of BLSTM to infer label or tag sequence, (3) model does not use any domain specific features or dictionary, i. e., in another words, same set of features are used in the three NER tasks, namely, disease name recognition (Disease NER), drug name recognition (Drug NER) and clinical entity recognition (Clinical NER).
1 code implementation • WS 2017 • Patchigolla V S S Rahul, Sunil Kumar Sahu, Ashish Anand
Event trigger identification is an important first step in all event extraction methods.
1 code implementation • 28 Jan 2017 • Sunil Kumar Sahu, Ashish Anand
The two models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the output of Bi-LSTM layer to assign weights to features.
no code implementations • ACL 2016 • Sunil Kumar Sahu, Ashish Anand
In particular, we propose various end-to-end recurrent neural network (RNN) models for the tasks of disease name recognition and their classification into four pre-defined categories.
no code implementations • WS 2016 • Sunil Kumar Sahu, Ashish Anand, Krishnadev Oruganty, Mahanandeeshwar Gattu
We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset.