Search Results for author: Yannis Katsis

Found 10 papers, 4 papers with code

SPOT: Knowledge-Enhanced Language Representations for Information Extraction

no code implementations20 Aug 2022 Jiacheng Li, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Andrew Bartko, Julian McAuley, Chun-Nan Hsu

To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.

Relation Extraction

Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction

1 code implementation AKBC 2021 William Hogan, Molly Huang, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Yoshiki Vazquez Baeza, Andrew Bartko, Chun-Nan Hsu

In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.

Relation Extraction

Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery

no code implementations12 Nov 2020 Canlin Zhang, Chun-Nan Hsu, Yannis Katsis, Ho-Cheol Kim, Yoshiki Vazquez-Baeza

Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics.

Link Prediction

A Survey of the State of Explainable AI for Natural Language Processing

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen

Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable.

NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

no code implementations AKBC 2019 Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu

Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.

Word Embeddings

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