arXiv AI By Marco Luca Sbodio, Marcos Mart\'inez Galindo, Vanessa Lopez, Blanca Biel, Pablo Canca, Pedro Delgado, Jes\'us I. Mendieta-Moreno, Raphael Tack, Maria J. Caturla

Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computation and Language
Sep 16

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP

SciNLP is a new benchmark dataset for full‑text entity and relation extraction in the NLP domain, comprising 60 manually annotated papers with 6,429 entities and 1,649 relations. It is the first dataset to provide full‑text annotations of entities and their relationships specifically for NLP literature. Experiments show that models trained on SciNLP outperform baselines on certain tasks, and the dataset enabled the automatic construction of a fine‑grained knowledge graph with an average node degree of 3.3.

By Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang