Hooman Sedghamiz — accepted AI-conference papers

2022

Generalizing over Long Tail Concepts for Medical Term Normalization

Beatrice Portelli, Simone Scaboro, Enrico Santus, Hooman Sedghamiz, Emmanuele Chersoni, Giuseppe Serra

EMNLP 2022main

Medical term normalization consists in mapping a piece of text to a large number of output classes.Given the small size of the annotated datasets and the extremely long tail distribution of the concepts, it is of utmost importance to develop models that are capable to generalize to scarce or unseen…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗

2021

Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media

Shivam Raval, Hooman Sedghamiz, Enrico Santus, Tuka Alhanai, Mohammad Ghassemi, Emmanuele Chersoni

EMNLP 2021finding

Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes it logistically intractable to analyze using human agents, with NLP representing the only low-cost and scalable alternati…

Cited by 18 Source ↗PDF ↗code ↗Scholar ↗

2021

SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations

Hooman Sedghamiz, Shivam Raval, Enrico Santus, Tuka Alhanai, Mohammad Ghassemi

EMNLP 2021finding

While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised alternative to Masked Language Modeling (MLM) for improving sequence representations. This paper introduces SupCL-Seq, whi…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗