no code implementations • 25 Oct 2023 • Ayesha Qamar, Chetan Verma, Ahmed El-Kishky, Sumit Binnani, Sneha Mehta, Taylor Berg-Kirkpatrick
Common language model (LM) encoders such as BERT can be used to understand and represent the textual content of webpages.
no code implementations • COLING (WNUT) 2022 • Jinning Li, Shubhanshu Mishra, Ahmed El-Kishky, Sneha Mehta, Vivek Kulkarni
We refer to these annotations as Non-Textual Units (NTUs).
no code implementations • 14 Oct 2022 • Shubhanshu Mishra, Aman Saini, Raheleh Makki, Sneha Mehta, Aria Haghighi, Ali Mollahosseini
Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications.
no code implementations • 6 Apr 2022 • Sneha Mehta, Huzefa Rangwala, Naren Ramakrishnan
We show how such context simplification can improve the performance of MRC-based event extraction by more than 5% for actor extraction and more than 10% for target extraction.
no code implementations • 22 May 2020 • Sneha Mehta, Bahareh Azarnoush, Boris Chen, Avneesh Saluja, Vinith Misra, Ballav Bihani, Ritwik Kumar
The model is used to preprocess source sentences of multiple low-resource language pairs.
1 code implementation • 26 Nov 2019 • Sneha Mehta, Huzefa Rangwala, Naren Ramakrishnan
Effective representation learning from text has been an active area of research in the fields of NLP and text mining.