With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology (DAO)) to jointly extract entities and their relationship using gated fusion sharing mechanism. Our model is further tailored to provide more focus to the entities mention in the sentence through entity-position aware attention layer, where ontology is used to locate the target entities position. Experimental results show that inclusion of the knowledge-aware attentive representation in association with BERT can extract the cannabis-depression relationship with better coverage in comparison to the state-of-the-art relation extractor.


翻译:随着大麻医疗和娱乐使用日益合法化,需要开展更多的研究,以了解与大麻消费有关的抑郁症与消费者行为之间的联系。大型社交媒体数据有可能向公共卫生分析家提供关于这些协会的更深刻见解。在这项跨学科研究中,我们展示了将特定领域知识纳入学习过程的价值,以确定大麻使用和抑郁之间的关系。我们开发了一个尾端至端知识注入深层学习框架(Gated-K-BERT),利用经过培训的BERT语言代表模式和特定领域宣言知识来源(DaO),利用封闭式聚变共享机制联合提取实体及其关系。我们的模式进一步调整,通过实体定位的注意层,将更多关注点放在句子中提到的实体,其中使用了肿瘤来定位目标实体的位置。实验结果表明,将知识认知性代表纳入与BERT相结合,可以利用预先培训的BERT语言代表模式和特定领域声明知识来源(DAO)来利用联合提取实体及其关系。我们的模式进一步调整了我们的模型,以便通过实体定位的注意层来定位目标实体的位置,从而确定目标实体的位置。实验结果表明,将知识意识代表纳入与BERT的结合中可以提取更好的覆盖面。

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