AgriCHN: A Comprehensive Cross-domain Resource for Chinese Agricultural Named Entity Recognition

TL;DR

AgriCHN is a comprehensive Chinese dataset for agricultural named entity recognition, including diverse entities from agriculture, hydrology, and meteorology, aiming to improve automated annotation accuracy.

Contribution

This paper introduces AgriCHN, a large, high-quality, multi-domain Chinese dataset for agricultural NER, filling a gap in existing resources and supporting advanced research.

Findings

AgriCHN contains 4,040 sentences and 15,799 entity mentions across 27 categories.

Benchmark experiments show AgriCHN's high complexity and challenge for current models.

The dataset enhances the diversity and granularity of agricultural entity recognition.

Abstract

Agricultural named entity recognition is a specialized task focusing on identifying distinct agricultural entities within vast bodies of text, including crops, diseases, pests, and fertilizers. It plays a crucial role in enhancing information extraction from extensive agricultural text resources. However, the scarcity of high-quality agricultural datasets, particularly in Chinese, has resulted in suboptimal performance when employing mainstream methods for this purpose. Most earlier works only focus on annotating agricultural entities while overlook the profound correlation of agriculture with hydrology and meteorology. To fill this blank, we present AgriCHN, a comprehensive open-source Chinese resource designed to promote the accuracy of automated agricultural entity annotation. The AgriCHN dataset has been meticulously curated from a wealth of agricultural articles, comprising a total of 4,040 sentences and encapsulating 15,799 agricultural entity mentions spanning 27 diverse entity categories. Furthermore, it encompasses entities from hydrology to meteorology, thereby enriching the diversity of entities considered. Data validation reveals that, compared with relevant resources, AgriCHN demonstrates outstanding data quality, attributable to its richer agricultural entity types and more fine-grained entity divisions. A benchmark task has also been constructed using several state-of-the-art neural NER models. Extensive experimental results highlight the significant challenge posed by AgriCHN and its potential for further research.