Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Martin, James H."'
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
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:
Ahmed, Shafiuddin Rehan, Nath, Abhijnan, Regan, Michael, Pollins, Adam, Krishnaswamy, Nikhil, Martin, James H.
Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference reso
Externí odkaz:
http://arxiv.org/abs/2306.05434
Event Coreference Resolution (ECR) is the task of linking mentions of the same event either within or across documents. Most mention pairs are not coreferent, yet many that are coreferent can be identified through simple techniques such as lemma matc
Externí odkaz:
http://arxiv.org/abs/2305.05672
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
Autor:
Suresh, Abhijit, Jacobs, Jennifer, Harty, Charis, Perkoff, Margaret, Martin, James H., Sumner, Tamara
Transcripts of teaching episodes can be effective tools to understand discourse patterns in classroom instruction. According to most educational experts, sustained classroom discourse is a critical component of equitable, engaging, and rich learning
Externí odkaz:
http://arxiv.org/abs/2204.09652