Scientific literature searches are often exploratory, whereby users are not
yet familiar with a particular field or concept but are interested in learning
more about it. However, existing systems for scientific literature search are
typically tailored to keyword-based lookup searches, limiting the possibilities
for exploration. We propose NLP-KG, a feature-rich system designed to support
the exploration of research literature in unfamiliar natural language
processing (NLP) fields. In addition to a semantic search, NLP-KG allows users
to easily find survey papers that provide a quick introduction to a field of
interest. Further, a Fields of Study hierarchy graph enables users to
familiarize themselves with a field and its related areas. Finally, a chat
interface allows users to ask questions about unfamiliar concepts or specific
articles in NLP and obtain answers grounded in knowledge retrieved from
scientific publications. Our system provides users with comprehensive
exploration possibilities, supporting them in investigating the relationships
between different fields, understanding unfamiliar concepts in NLP, and finding
relevant research literature. Demo, video, and code are available at:
this https URL

科学文献搜索是一种探索性方法，现有的科学文献搜索系统通常限于基于关键词的查询搜索，我们提出 NLP-KG 系统，支持在不熟悉的自然语言处理 (NLP) 领域探索研究文献，并提供语义搜索、调研论文检索、研究领域层次图和聊天接口等功能，以帮助用户全面地探索和理解不同领域之间的关系、NLP 中的陌生概念以及找到相关的研究文献。