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Trevor Cohn

5 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • stat.ML1
ORCID 0000-0003-4363-1673
same name
  • Trevor Cohn — 49 papers, h 53
  • Trevor Cohn — 19 papers, h 17
  • Trevor Cohn — 8 papers, h 5
  • Trevor Cohn — 6 papers, h 3
  • Trevor Cohn — 6 papers, h 3
  • Trevor Cohn — 5 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162025
most citedDyNet: The Dynamic Neural Network Toolkit

343 citations · 350 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Learning Robust Negation Text Representations

Thinh Hung Truong, Karin Verspoor, Trevor Cohn +1

Despite rapid adoption of autoregressive large language models, smaller text encoders still play an important role in text understanding tasks that require rich contextualized repr…

cs.CL2016

Learning Robust Representations of Text

Yitong Li, Trevor Cohn, Timothy Baldwin

Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks. We present…

cs.CL2016★ 7 cited

Using Gaussian Processes for Rumour Stance Classification in Social Media

Michal Lukasik, Kalina Bontcheva, Trevor Cohn +3

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media us…

cs.CL2016

Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees

Ehsan Shareghi, Matthias Petri, Gholamreza Haffari +1

Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora. We propose a language model based on compressed suffix trees…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.