1 code implementation • ACL 2022 • Tyler Bikaun, Michael Stewart, Wei Liu
Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators.
1 code implementation • EMNLP (ACL) 2021 • Tyler Bikaun, Tim French, Melinda Hodkiewicz, Michael Stewart, Wei Liu
LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide.
1 code implementation • 7 May 2024 • Tyler Bikaun, Michael Stewart, Wei Liu
CleanGraph allows users to perform Create, Read, Update, and Delete (CRUD) operations on their graphs, as well as apply models in the form of plugins for graph refinement and completion tasks.
1 code implementation • 15 Sep 2023 • Michael Stewart, Melinda Hodkiewicz, Sirui Li
In this paper we present the first investigation into the effectiveness of Large Language Models (LLMs) for Failure Mode Classification (FMC).
no code implementations • 23 Mar 2020 • Michael Stewart, Wei Liu
They are therefore sensitive to window size selection and are unable to incorporate the context of the entire document.
no code implementations • 13 Nov 2019 • Michael Stewart, Wei Liu, Rachel Cardell-Oliver
In this paper we introduce a word-level GRU-based LN model and investigate the effectiveness of recent embedding techniques on word-level LN.
no code implementations • IJCNLP 2019 • Michael Stewart, Wei Liu, Rachel Cardell-Oliver
We introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing.
2 code implementations • 4 Sep 2019 • Michael Stewart, Majigsuren Enkhsaikhan, Wei Liu
We present an overview of our triple extraction system for the ICDM 2019 Knowledge Graph Contest.
1 code implementation • 17 Dec 2018 • Weichang Yu, John T. Ormerod, Michael Stewart
A Bayesian method that seamlessly fuses classification via discriminant analysis and hypothesis testing is developed.
Methodology
no code implementations • 8 Mar 2014 • Michael Stewart
Specificity is important for extracting collocations, keyphrases, multi-word and index terms [Newman et al. 2012].