Learning Disentangled Representations of Negation and Uncertainty

1 code implementation • ACL 2022 • Jake Vasilakes, Chrysoula Zerva, Makoto Miwa, Sophia Ananiadou

Negation and uncertainty modeling are long-standing tasks in natural language processing.

Disentanglement Negation

An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles

no code implementations • 4 Aug 2021 • Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz, Jake Vasilakes, Jacob C. Solinsky, Reed McEwan, Benjamin C. Knoll, Christopher J. Tignanelli, Hongfang Liu, Hua Xu, Xiaoqian Jiang, Genevieve B. Melton, Serguei VS Pakhomov

Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess how filtering by aggregation of Unified Medical Language System (UMLS) Metathesaurus concepts affects system performance for Named Entity Recognition (NER) of UMLS concepts.

named-entity-recognition Named Entity Recognition +1

Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature

no code implementations • 24 Jun 2021 • Dalton Schutte, Jake Vasilakes, Anu Bompelli, Yuqi Zhou, Marcelo Fiszman, Hua Xu, Halil Kilicoglu, Jeffrey R. Bishop, Terrence Adam, Rui Zhang

MATERIALS AND METHODS: We created SemRepDS (an extension of SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28, 884 DS terms not found in the UMLS.