Papers with NLP frameworks
The Classical Language Toolkit: An NLP Framework for Pre-Modern Languages (2021.acl-demo)
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Kyle P. Johnson, Patrick J. Burns, John Stewart, Todd Cook, Clément Besnier, William J. B. Mattingly
| Challenge: | Classical Language Toolkit (CLTK) is an NLP framework for pre-modern languages . authors say it assumes pre-existing living languages, neglecting important characteristics of non-spoken historical languages despite their existence . |
| Approach: | The paper announces version 1.0 of the Classical Language Toolkit (CLTK) it is an NLP framework for pre-modern languages that uses assumptions specific to living languages . authors propose a modular processing pipeline that balances competing demands of algorithmic diversity with pre-configured defaults . |
| Outcome: | The Classical Language Toolkit (CLTK) is a new NLP framework for pre-modern languages . the framework is based on the existing frameworks and is available for almost 20 languages - including models . |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
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| Challenge: | Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature. |
| Approach: | They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk . |
| Outcome: | The proposed model outperforms existing models on Twitter and other social media platforms. |
Empathy Applicability Modeling for General Health Queries (2026.findings-acl)
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| Challenge: | Existing NLP frameworks focus on reactively labeling empathy in doctors’ responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. |
| Approach: | They propose an Empathy Applicability Framework that classifies patient queries in terms of the applicability of emotional reactions and interpretations based on clinical, contextual, and linguistic cues. |
| Outcome: | The Empathy Applicability Framework outperforms heuristic and zero-shot LLMs in the clinical setting. |