【摘要】 本文提出了有监督的关键词抽取算法——KEING(Keyphrase Extraction using sequentIal patterns with oNe-off and General gaps condition)算法.首先,将每篇文档作为一个序列库,利用SPING(Sequential Patterns mIning with oNe-off and General gaps condition)算法获取词语之间的关系及其多种变化形式,并利用统计模式特征的方式描述候选关键词;然后,通过朴素贝叶斯分类算法对大量带标记的训练数据进行训练,构造分类器;最后利用分类器从测试文档中识别出关键词.通过实验验证了SPING算法的完备性以及KEING算法的有效性.更多还原

【Abstract】 Keyphrases are used to summarize the document and high-quality keyphrases have great importance in text summarizing,reading and indexing.However,most studies of keyphrase extraction have strict limitation in the form of patterns,and are unable to achieve the semantic relation between words and phrases.The results are failure to autonomously extract keyphrases.Keyphrase extraction using sequential patterns mining with one-off and general gaps condition algorithm(KEING) is proposed in this paper.Taking into account one off condition and general gaps,SPING(Sequential Patterns mIning with oNe-off and General gaps condition)can catch semantic relations between words and phrases more effectively.Therefore,KEING will get effective candidate keyphrases and count their features.Then a supervised machine learning method is used to train features and construct a classification model,we can extract keyphrase with this model.Experimental results demonstrate KEING can effectively extract high quality keyphrases.更多还原