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Frank Rudzicz

7 papers here

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

author position
  • first author1
  • last author4

Across the 5 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.CV1
  • cs.LG1
ORCID 0000-0002-1139-3423
same name
  • Frank Rudzicz — 56 papers, h 43
  • Frank Rudzicz — 17 papers, h 5
  • Frank Rudzicz — 8 papers, h 4
  • Frank Rudzicz — 6 papers, h 5
  • Frank Rudzicz — 3 papers, h 4
  • Frank Rudzicz — 3 papers, h 3

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
20102023
most citedTowards a Heuristic Categorization of Prepositional Phrases in English with WordNet

5 citations · 7 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2022

Predicting Fine-Tuning Performance with Probing

Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz

Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. Alternatively, probing has receive…

cs.CL2016

An Adaptive Psychoacoustic Model for Automatic Speech Recognition

Peng Dai, Xue Teng, Frank Rudzicz +1

Compared with automatic speech recognition (ASR), the human auditory system is more adept at handling noise-adverse situations, including environmental noise and channel distortion…

cs.CL2016★ 1 cited

Sex, drugs, and violence

Stefania Raimondo, Frank Rudzicz

Automatically detecting inappropriate content can be a difficult NLP task, requiring understanding context and innuendo, not just identifying specific keywords. Due to the large qu…

cs.CL2010★ 5 cited

Towards a Heuristic Categorization of Prepositional Phrases in English with WordNet

Frank Rudzicz, Serguei A. Mokhov

This document discusses an approach and its rudimentary realization towards automatic classification of PPs; the topic, that has not received as much attention in NLP as NPs and VP…

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