[Purpose/significance] The abstract of scientific papers is a vital indexing object within information organization. Meanwhile, indexing the abstract according to certain rules is conducive for not only scientific communication or knowledge discovery, and intelligence analysis as well. Thus, how to realize auto-index accurately and quickly, for millions of unstructured abstracts existed nowadays is a crucial problem to be addressed.[Method/process] This study assumed that different categories of abstract are inherently consistent, that is, the study of structured abstract can provide a method and technical reference for unstructured abstract auto-indexing. Acting in accordance with this assumption and based on the US National Library of Medicine's structural element labeling terminology, this study accomplished mapping across abstract element classifications and proposed BOMRC system, a normalization indexing method for structured abstract. Then we collected research sample and used text mining method to analyze multiple features of structured abstract quantitatively and statistically, such as word frequency, TF-IDF value, as for dimension of words, verbs, three-word lexical chunks and four-word lexical chunks, which enabled us propose a semantic feature dictionary for structured elements. Finally, we used unstructured abstract to test the validity of the semantic feature dictionary.[Result/conclusion] The results show that the semantic feature dictionary method can effectively identify various structural elements of scientific paper abstract, and it can be used to optimize the automatic recognition model, which may be based on machine learning methods.
Song Donghuan
,
Li Chenying
,
Liu Ziyu
,
Han Mingjie
. Semantic Feature Dictionary Construction of Abstract in English Scientific Journals[J]. Library and Information Service, 2020
, 64(6)
: 108
-119
.
DOI: 10.13266/j.issn.0252-3116.2020.06.013
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