互联网存在海量的文献和科技信息,隐含着大量高价值情报。识别国防科技领域中的技术和术语可以为构建国防科技知识图谱奠定基础。该文基于此领域的海量军事文本,以维基百科中军事领域的新技术为基点采集语料,涵盖了新闻、文献和维基百科三种体裁。在分析军事技术文本特点的基础上制定了一系列标注规范,开展了大规模语料的标注工作,构建了一个面向国防科技领域的技术和术语语料库。该语料库共标注了479篇文章,包含24 487个句子和33 756个技术和术语。同时,该文探讨了模型预标注策略的可行性,并对技术和术语类别在不同体裁上的分布以及语料标注的一致性进行了统计分析。基于该语料库的实验表明,技术和术语识别性能F1值达到70.40%,为进一步的技术和术语识别研究提供了基础。
Abstract
Massive literature and science information on Internet can supply valuable intelligence. The detection of technology and terminology is fundamental for constructing oriented national defense science (ONDS) technology knowledge base. We analyze military text characteristics and design annotation guidelines for ONDS technology and terminology from massive internet content for a list of military emerging technology defined in Wikipedia. Based on the annotation guidelines, we conduct broad-scale corpus annotation process, and we construct a ONDS technology and terminology corpus which covers three genres of news, papers and Wikipedia. we finally annotated 479 articles with 24,487 sentences and 33,756 technologies and terminologies. Meanwhile, we explore the feasibility of model pre-annotating, analyze distribution of technology and terminology in different genres and calculate annotation consistency for the corpus. Experiment result based on the corpus show that the detection of technology and terminology achieves 70.40% F1 scores. The work presented in this paper builds foundations for detection of ONDS technology and terminology.
关键词
面向国防科技领域 /
技术和术语 /
标注规范 /
语料库
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Key words
oriented national defense science /
technology and terminology /
annotation guidelines /
corpus
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脚注
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基金
国家自然科学基金(61836007,61472265,61876120)
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