报告题目: Ontology and Word Embedding for Biomedical Language Processing

报告人：Pierre Zweigenbaum

报告时间：2019年11月6日（周三）9：00

报告地点：逸夫楼C座603会议室

摘要：Current natural language processing methods heavily rely on vector space word representations, known as word embeddings, self-trained on large text corpora. Knowledge graphs are also increasingly represented in vector spaces as graph embeddings. Ontologies are instances of knowledge graphs. I will present a short review of the methods created to embed words and ontologies. I will then focus on a method that we designed to create a shared embedding space for both corpus words and ontology concepts. It is applied to a concept normalization task, which consists in mapping an input term to the most relevant concept in a target ontology. This concept normalization task is a part of the Bacteria Biotope BioNLP shared tasks, for which our method holds a top rank.

报告人简介：

法国国家科学研究院高级研究员.

研究方向：以医学为主要应用方向的自然语言处理（BioNLP）。 研究兴趣为跨语言信息提取、医学实体识别、缩略词、消歧、关系发现、语料库和主题词表获取。