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| Automatic Indexing of Large Scale Subject Words |
| Han Hongqi1,2, Gui Jie1, Zhang Yunliang1,2, Weng Mengjuan1,2, Xue Shan1,2, Yue Lindong1,2 |
1.Institute of Scientific and Technical Information of China, Beijing 100038
2.Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content, National Press and Publication Administration, Beijing 100038 |
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Abstract Existing subject indexing methods can only extract words that appear in the text but cannot select the words that have strong semantic correlation and do not appear from tens of thousands or hundreds of thousands of subject words. The multi-label text classification algorithm based on machine learning needs training data under each label, limiting its application in subject indexing. Aiming at the indexing requirements of large-scale subject words in massive documents, this study proposes an automatic indexing method based on the distributed word vector technique, which uses the word vector trained by a large-scale corpus to generate representation vectors for subject words and text documents of the same dimension and realizes the calculation of semantic similarities between them. The mapping table between subject and common words is constructed based on a large-scale corpus, so that the text vector is only compared with a small number of semantically strongly related subject word vectors, which significantly reduces the amount of calculation and improves the indexing efficiency. The developed automatic indexing tool has been applied to subject indexing on nearly 100 million documents and has achieved satisfactory speed. Compared with the Jieba keywords, the proposed method has a lower coincidence degree between the subject words and author keywords and achieves better indexing accuracy than the Jieba keywords after removing the non-subject words in the Jieba keywords.
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Received: 07 February 2021
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