[Purpose/Significance] With the rapid development of information technology, the amount of short text data such as user comments and patient symptoms has grown rapidly. How to mine valuable information from short texts has become a research hotspot in text classification. [Method/Process] This paper took the case symptom dataset of patients in various departments of a hospital in China as the target for semantic information deficiency of short symptom text. Based on the importance and relevancy of symptom words in each department, the study took the symptom text with less than the number of set symptom words as the semantic enhancement object, extracted some keywords of typical symptoms from each department by using Word2Vec and probability-based TFIDF algorithm, and add them to the semantic enhancement object to form a new lexical data set. Finally, a machine learning algorithm was used to classify the text of symptoms. [Result/Conclusion] Compared with the original predictive set, the new corpus based on the semantic enhancement method proposed in this paper has a better classification effect on SVM, MNB and RF, with an accuracy of about 10%, 9% and 10%, respectively.
Zhang Mengyun
,
Ding Jingda
. Research on Semantic Enhancement for Short Text Classification[J]. Library and Information Service, 2023
, 67(9)
: 4
-11
.
DOI: 10.13266/j.issn.0252-3116.2023.09.001
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