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pro vyhledávání: '"Cai, Jon Z."'
This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpre
Externí odkaz:
http://arxiv.org/abs/2405.09153
This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experi
Externí odkaz:
http://arxiv.org/abs/2403.15407
Autor:
Cai, Jon Z., Ahmed, Shafiuddin Rehan, Bonn, Julia, Wright-Bettner, Kristin, Palmer, Martha, Martin, James H.
In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a cutting-edge web-based tool designed for constructing Abstract Meaning Representation (AMR) from natural language text. CAMRA offers a novel approach to deep lexical semantics annot
Externí odkaz:
http://arxiv.org/abs/2311.10928
Autor:
Cai, Jon Z., King, Brendan, Perkoff, Margaret, Dudy, Shiran, Cao, Jie, Grace, Marie, Wojarnik, Natalia, Ganesh, Ananya, Martin, James H., Palmer, Martha, Walker, Marilyn, Flanigan, Jeffrey
Publikováno v:
The 13th International Workshop on Spoken Dialogue Systems Technology 2023
In this paper, we introduce Dependency Dialogue Acts (DDA), a novel framework for capturing the structure of speaker-intentions in multi-party dialogues. DDA combines and adapts features from existing dialogue annotation frameworks, and emphasizes th
Externí odkaz:
http://arxiv.org/abs/2302.12944
Feature importance is commonly used to explain machine predictions. While feature importance can be derived from a machine learning model with a variety of methods, the consistency of feature importance via different methods remains understudied. In
Externí odkaz:
http://arxiv.org/abs/1910.08534