Thai Nested Named Entity Recognition Corpus

Abstract

This paper presents the first Thai Nested Named Entity Recognition (N-NER) dataset. Thai N-NER consists of 264,798 mentions, 104 classes, and a maximum depth of 8 layers obtained from news articles and restaurant reviews, a total of 4894 documents. Our work, to the best of our knowledge, presents the largest non-English N-NER dataset and the first non-English one with fine-grained classes. To understand the new challenges our proposed dataset brings to the field, we conduct an experimental study on (i) cutting edge N-NER models with the state-of-the-art accuracy in English and (ii) baseline methods based on wellknown language model architectures. From the experimental results, we obtain two key findings. First, all models produce poor F1 scores in the tail region of the class distribution. There is little or no performance improvement provided by these models with respect to the baseline methods with our Thai dataset. These findings suggest that further investigation is required to make a multilingual N-NER solution that works well across different languages.

NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities

“…Moreover, the annotated entities in these corpora are limited to the most common entity types such as drugs/chemicals and diseases ( Leaman et al 2009 ; Gurulingappa et al 2010 ; Van Mulligen et al 2012 ; Wei et al 2016 ); see an overview of 20+ NER biomedical datasets in a BIGBIO (BigScience Biomedical) library ( Fries et al 2022 ) for more information. Recent work has shown an increased interest in nested entity structures in general-domain data in various languages, including English ( Ringland et al 2019 ), Russian ( Loukachevitch et al 2021 ), Thai ( Buaphet et al 2022 ), and Danish ( Plank et al 2020 ). The most widely studied corpus for nested NER in the biomedical domain is GENIA ( Kim et al 2003 ) which consists of 2000 PubMed abstracts and 100 000 annotations divided into 47 entity types.…”