Do syntactic trees enhance Bidirectional Encoder Representations from Transformers (BERT) models for chemical-drug relation extraction?

Collecting relations between chemicals and drugs is crucial in biomedical research. The pre-trained transformer model, e.g. Bidirectional Encoder Representations from Transformers (BERT), is shown to have limitations on biomedical texts; more specifically, the lack of annotated data makes relation extraction (RE) from biomedical texts very challenging. In this paper, we hypothesize that enriching a pre-trained transformer model with syntactic information may help improve its performance on chemical-drug RE tasks. For this purpose, we propose three syntax-enhanced models based on the domain-specific BioBERT model: Chunking-Enhanced-BioBERT and Constituency-Tree-BioBERT in which constituency information is integrated and a Multi-Task-Learning framework Multi-Task-Syntactic (MTS)-BioBERT in which syntactic information is injected implicitly by adding syntax-related tasks as training objectives. Besides, we test an existing model Late-Fusion which is enhanced by syntactic dependency information and build ensemble systems combining syntax-enhanced models and non-syntax-enhanced models. Experiments are conducted on the BioCreative VII DrugProt corpus, a manually annotated corpus for the development and evaluation of RE systems. Our results reveal that syntax-enhanced models in general degrade the performance of BioBERT in the scenario of biomedical RE but improve the performance when the subject-object distance of candidate semantic relation is long. We also explore the impact of quality of dependency parses. [Our code is available at: https://github.com/Maple177/syntax-enhanced-RE/tree/drugprot (for only MTS-BioBERT); https://github.com/Maple177/drugprot-relation-extraction (for the rest of experiments)] Database URL https://github.com/Maple177/drugprot-relation-extraction.

Natural skin-whitening compounds for the treatment of melanogenesis (Review).

Melanogenesis is the process for the production of melanin, which is the primary cause of human skin pigmentation. Skin-whitening agents are commercially available for those who wish to have a lighter skin complexions. To date, although numerous natural compounds have been proposed to alleviate hyperpigmentation, insufficient attention has been focused on potential natural skin-whitening agents and their mechanism of action from the perspective of compound classification. In the present article, the synthetic process of melanogenesis and associated core signaling pathways are summarized. An overview of the list of natural skin-lightening agents, along with their compound classifications, is also presented, where their efficacy based on their respective mechanisms of action on melanogenesis is discussed.