no code implementations • NLPerspectives (LREC) 2022 • Christopher Homan, Tharindu Cyril Weerasooriya, Lora Aroyo, Chris Welty
Annotator disagreement is often dismissed as noise or the result of poor annotation process quality.
no code implementations • COLING 2020 • Alyssa Lees, Chris Welty, Shubin Zhao, Jacek Korycki, Sara Mc Carthy
A common step in developing an understanding of a vertical domain, e. g. shopping, dining, movies, medicine, etc., is curating a taxonomy of categories specific to the domain.
1 code implementation • 5 Nov 2019 • Chris Welty, Praveen Paritosh, Lora Aroyo
In this paper we present the first steps towards hardening the science of measuring AI systems, by adopting metrology, the science of measurement and its application, and applying it to human (crowd) powered evaluations.
no code implementations • 30 Oct 2019 • Ananth Balashankar, Alyssa Lees, Chris Welty, Lakshminarayanan Subramanian
The potential for learned models to amplify existing societal biases has been broadly recognized.
1 code implementation • NAACL 2019 • Anca Dumitrache, Lora Aroyo, Chris Welty
We present a resource for the task of FrameNet semantic frame disambiguation of over 5, 000 word-sentence pairs from the Wikipedia corpus.
1 code implementation • WS 2018 • Anca Dumitrache, Lora Aroyo, Chris Welty
Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels.
2 code implementations • 18 Aug 2018 • Anca Dumitrache, Oana Inel, Lora Aroyo, Benjamin Timmermans, Chris Welty
However, in many domains, there is ambiguity in the data, as well as a multitude of perspectives of the information examples.
Human-Computer Interaction Social and Information Networks
1 code implementation • 1 May 2018 • Anca Dumitrache, Lora Aroyo, Chris Welty
FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words.
1 code implementation • 14 Nov 2017 • Anca Dumitrache, Lora Aroyo, Chris Welty
Distant supervision (DS) is a well-established method for relation extraction from text, based on the assumption that when a knowledge-base contains a relation between a term pair, then sentences that contain that pair are likely to express the relation.
1 code implementation • 9 Jan 2017 • Anca Dumitrache, Lora Aroyo, Chris Welty
Cognitive computing systems require human labeled data for evaluation, and often for training.