[Purpose/significance] To find the solutions of automatically identifying search query intent and improve the efficiency of academic search engines. [Method/process] Combining the features of query intent and academic search, we constructed the feature from four aspects, which are the basic descriptive statistics, the special keywords, entity information and the frequency. For the experiments, we examined four types of classifiers which are the Naive Bayes, Logistic regression, SVM, Random Forest and calculated precision, recall and F-measure. A method which is extending the recognition results of academic query intent predicted by Logistic regression algorithm to large-scale data sets and extracting "keyword type" features is proposed to construct a two-layer classifier based on deep learning algorithm for academic query intent recognition. [Result/conclusion] The macro-average F1 value of the two-layer classifier is 0.651, which is superior to other algorithms. This method can effectively balance the precision and recall rate of different academic query intentions. The final second-layer prediction model receives the best classification performance, the score of F1 is 0.783.
Wang Ruixue
,
Fang Jing
,
Gui Sisi
,
Lu Wei
,
Zhang Xian
. Based on Deep Learning Algorithm to Construct the Classifier of Academic Query Intent[J]. Library and Information Service, 2021
, 65(3)
: 93
-99
.
DOI: 10.13266/j.issn.0252-3116.2021.03.012
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