条件随机域(场)(conditional random fields,简称 CRF,或CRFs),是一种判别式概率模型,是随机场的一种,常用于标注或分析序列资料,如自然语言文字或是生物序列。 如同马尔可夫随机场,条件随机场为具有无向的图模型,图中的顶点代表随机变量,顶点间的连线代表随机变量间的相依关系,在条件随机场中,随机变量 Y 的分布为条件机率,给定的观察值则为随机变量 X。原则上,条件随机场的图模型布局是可以任意给定的,一般常用的布局是链结式的架构,链结式架构不论在训练(training)、推论(inference)、或是解码(decoding)上,都存在效率较高的算法可供演算。

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With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Na\"ive Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the following research on automatic keyphrase extraction.

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