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pro vyhledávání: '"Martin James A"'
Autor:
Mehrabi, Niloufar, Boroujeni, Sayed Pedram Haeri, Hofseth, Jenna, Razi, Abolfazl, Cheng, Long, Kaur, Manveen, Martin, James, Amin, Rahul
Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection, thanks to their ability to access remote areas and transmit real-tim
Externí odkaz:
http://arxiv.org/abs/2410.10843
Network point processes often exhibit latent structure that govern the behaviour of the sub-processes. It is not always reasonable to assume that this latent structure is static, and detecting when and how this driving structure changes is often of i
Externí odkaz:
http://arxiv.org/abs/2407.04138
Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing
Autor:
Ahmed, Shafiuddin Rehan, Wang, Zhiyong Eric, Baker, George Arthur, Stowe, Kevin, Martin, James H.
The most popular Cross-Document Event Coreference Resolution (CDEC) datasets fail to convey the true difficulty of the task, due to the lack of lexical diversity between coreferring event triggers (words or phrases that refer to an event). Furthermor
Externí odkaz:
http://arxiv.org/abs/2407.11988
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
Autor:
Nath, Abhijnan, Jamil, Huma, Ahmed, Shafiuddin Rehan, Baker, George, Ghosh, Rahul, Martin, James H., Blanchard, Nathaniel, Krishnaswamy, Nikhil
Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language
Externí odkaz:
http://arxiv.org/abs/2404.08949
Autor:
Ahmed, Shafiuddin Rehan, Baker, George Arthur, Judge, Evi, Regan, Michael, Wright-Bettner, Kristin, Palmer, Martha, Martin, James H.
Event Coreference Resolution (ECR) as a pairwise mention classification task is expensive both for automated systems and manual annotations. The task's quadratic difficulty is exacerbated when using Large Language Models (LLMs), making prompt enginee
Externí odkaz:
http://arxiv.org/abs/2404.08656
We study a multispecies $t$-PushTASEP system on a finite ring of $n$ sites with site-dependent rates $x_1,\dots,x_n$. Let $\lambda=(\lambda_1,\dots,\lambda_n)$ be a partition whose parts represent the species of the $n$ particles on the ring. We show
Externí odkaz:
http://arxiv.org/abs/2403.10485
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:
Suresh, Abhijit, author, Penuel, William R., author, Jacobs, Jennifer K., author, Raza, Ali, author, Martin, James H., author, Sumner, Tamara, author
Publikováno v:
Uses of Artificial Intelligence in STEM Education, 2024.
Externí odkaz:
https://doi.org/10.1093/oso/9780198882077.003.0017
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