Scientific literature analysis needs fine-grained named entity recognition ( NER ) to provide a wide range of information for scientific discovery . For example , chemistry research needs to study dozens to hundreds of distinct , fine-grained entity types , making consistent and accurate annotation difficult even for crowds of domain experts . On the other hand , domain-specific ontologies and knowledge bases ( KBs ) can be easily accessed , constructed , or integrated , which makes distant supervision realistic for fine-grained chemistry NER . In distant supervision , training labels are generated by matching mentions in a document with the concepts in the knowledge bases ( KBs ) . However , this kind of KB-matching suffers from two major challenges : incomplete annotation and noisy annotation . We propose CHEMNER , an ontologyguided , distantly-supervised method for finegrained chemistry NER to tackle these challenges . It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation . It significantly improves the distant label generation for the subsequent sequence labeling model training . We also provide an expertlabeled , chemistry NER dataset with 62 finegrained chemistry types ( e.g. , chemical compounds and chemical reactions ) . Experimental results show that CHEMNER is highly effective , outperforming substantially the stateof-the-art NER methods ( with .25 absolute F1 score improvement ) . Named entity recognition ( NER ) is a fundamental step in scientific literature analysis to build AI-driven systems for molecular discovery , synthetic strategy designing , and manufacturing ( Xie et al . , 2013 ; Szklarczyk et al . , 2015 ; Huang et al . , 2015 ; Szklarczyk et al . , 2017 ; de Almeida et al . , 2019 ) . It aims to locate and classify entity mentions ( e.g. , " Suzuki-Miyaura cross-coupling reactions " ) from unstructured text into pre-defined categories ( e.g. , " coupling reactions " ) . In the chemistry domain , previous NER studies are mostly focused on one coarse-grained entity type ( i.e. , chemicals ) ( Krallinger et al . , 2015 ; He et al . , 2020 ; Watanabe et al . , 2019 ) and rely on large amounts of manuallyannotated data for training deep learning models ( Chiu and Nichols , 2016 ; Ma and Hovy , 2016 ; Lample et al . , 2016 ; Wang et al . , 2019b ; Devlin et al . , 2019 ; Liu et al . , 2019 ) . In real-world applications , it is important to recognize chemistry entities on diverse and finegrained types ( e.g. , " inorganic phophorus compounds " , " coupling reactions " and " catalysts " ) to provide a wide range of information for scientific discovery . It will need dozens to hundreds of distinct types , making consistent and accurate annotation difficult even for domain experts . On the other hand , the domain-specific ontologies and knowledge bases ( KBs ) can be easily accessed , constructed , or integrated , which makes distant supervision realistic for fine-grained chemistry NER . Still , challenges exist for correctly recognizing the entity boundaries and accurately typing entities with distant supervision . In distant supervision , training labels are generated by matching the mentions in a document with the concepts in the knowledge bases ( KBs ) . However , this kind of KB-matching suffers from two major challenges : ( 1 ) incomplete annotation where a mention in a document can be matched only partially or missed completely due to an incomplete coverage of the KBs ( Figure 1a ) , and ( 2 ) noisy annotation where a mention can be erroneously matched due to the potential matching of multiple entity types in the KBs ( Figure 1b ) . Due to the complex name structures ( e.g. , nested naming structures and long chemical formulas ) of chemical entities , these challenges lead to severe low-precision and low-recall for finegrained chemistry NER with distant supervision . Several studies have attempted to address the incomplete annotation problem in distantlysupervised NER . For example , AutoNER ( Shang et al . , 2018b ) introduces an " unknown " type that can be skipped during training to reduce the effect of false negative labeling with distant supervision . BOND ( Liang et al . , 2020 ) leverages the power of pre-trained language models and a self-training approach to iteratively incorporate more training labels and improve the NER performance . However , previous methods assume a high precision and reasonable coverage of KB-matching for distant label generation . For example , the KB-matching on the CoNLL03 dataset ( Liang et al . , 2020 ) reported over 80 % on precision and over 60 % on recall . These methods do not work well with fine-grained chemistry NER that has severe low precision and low recall with KB-matching . Previous studies also largely ignore the noisy annotation problem by simply discarding those multi-labels during the KBmatching process ( Liang et al . , 2020 ) . However , the noisy labels can not be simply ignored for the chemistry entities because they consist of a large portion of distant training labels . We observe that more than 60 % of the entities have multiple labels during KB-matching in the chemistry domain . We propose CHEMNER , an ontology-guided , distantly-supervised NER method for fine-grained chemistry NER . Taking an input corpus , a chemistry type ontology and associated entity dictionaries collected from the KBs , we develop a novel flexible KB-matching method with TF-IDF-based majority voting to resolve the incomplete annota-tion problem . Then we develop a novel ontologyguided multi-type disambiguation method to resolve the noisy annotation problem . Taking the output from the above two steps as distant supervision , we further train a sequence labeling model to cover additional entities . CHEMNER significantly improves the distant label generation for the subsequent NER model training . We also provide an expert-labeled , chemistry NER dataset with 62 finegrained chemistry types ( e.g. , chemical compounds and chemical reactions ) . Experimental results show that CHEMNER is highly effective , achieving substantially better performance ( with .25 absolute F1 score improvement ) compared with the state-ofthe-art NER methods . We have released our data and code to benefit future studies1 . We propose CHEMNER , an ontology-guided , distantly-supervised method for fine-grained chemistry NER . It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation . We also provide an expert labeled , chemistry NER dataset with 62 finegrained chemistry types ( e.g. , chemical compounds and chemical reactions ) . Experimental results show that CHEMNER is highly effective , outperforming substantially the state-of-the-art NER methods on fine-grained chemistry NER . Although achieving great performance , there is still large room for improvement of CHEMNER . In the future , we plan to further refine and enrich the type ontology and incorporate more information in the dictionaries ( e.g. , chemical structures in the KBs ) for a better NER performance . We also plan to apply our finegrained NER method to other scientific domains .

Challenge: Existing distant supervision-based knowledge based-matching methods for the chemistry domain suffer from incomplete and noisy annotations due to the complex name structures in the domain.
Approach: They propose to use type ontology structures to generate distant labels and apply a multi-type disambiguation method to resolve the noisy annotation problem.
Outcome: The proposed method improves the distant label generation for the subsequent model training and outperforms the state-of-the-art NER methods on the newly expert-labelled chemistry dataset.