1 citations · 1 across the 1 of their papers we have counts for
5 papers
Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization
Brydon Parker, Alik Sokolov, Mahtab Ahmed +3
Summarization of long-form text data is a problem especially pertinent in knowledge economy jobs such as medicine and finance, that require continuously remaining informed on a sop…
Improving Tree-LSTM with Tree Attention
Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer
In Natural Language Processing (NLP), we often need to extract information from tree topology. Sentence structure can be represented via a dependency tree or a constituency tree st…
A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation
Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer
Word sense disambiguation (WSD) is a well researched problem in computational linguistics. Different research works have approached this problem in different ways. Some state of th…
Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention
Mahtab Ahmed, Jumayel Islam, Muhammad Rifayat Samee +1
Identifying interactions between proteins is important to understand underlying biological processes. Extracting a protein-protein interaction (PPI) from the raw text is often very…
Improving Neural Sequence Labelling using Additional Linguistic Information
Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer
Sequence labelling is the task of assigning categorical labels to a data sequence. In Natural Language Processing, sequence labelling can be applied to various fundamental problems…