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Found 7 papers, 1 papers with code
Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study
Objective: To explore and compare the performance of ChatGPT and other state-of-the-art LLMs on domain-specific NER tasks covering different entity types and domains in TCM against COVID-19 literature.
Toward FAIR Semantic Publishing of Research Dataset Metadata in the Open Research Knowledge Graph
We design a specific application of the ORKG-Dataset semantic model based on 40 diverse research datasets on scientific information extraction.
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models
This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering.
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets
Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding.
Ranked #1 on
Scholarly Named Entity Recognition
on GSAP-NER
EXmatcher: Combining Features Based on Reference Strings and Segments to Enhance Citation Matching
Citation matching is a challenging task due to different problems such as the variety of citation styles, mistakes in reference strings and the quality of identified reference segments.
Highly cited references in PLOS ONE and their in-text usage over time
We have found that these references are distinguishable by the IMRaD sections of their citation.
Digital Libraries
Team GESIS Cologne: An all in all sentence-based approach for FEVER
In this system description of our pipeline to participate at the Fever Shared Task, we describe our sentence-based approach.
