02267nas a2200397   4500000000100000000000100001008004100002260005700043653002400100653002500124653001000149653003600159653001600195653003300211653003400244653003400278653002700312653003500339653001900374653002200393653001400415653002300429653002400452653002000476653002500496100001200521700001500533700001000548700001600558245009200574856015300666300001200819490001500831520098100846020004201827       2021                            d  bSpringer Science and Business Media Deutschland GmbH10aAttention mechanism10aAttention mechanisms10aBrain10aClassification (of information)10aConvolution10aconvolutional neural network10aConvolutional neural networks10aIntangible cultural heritages10aLong short-term memory10aLong short-term memory network10aMemory network10aSemantic features10aSemantics10aStructural feature10aText classification10aText processing10aTraditional cultures1 aYang Xu1 aYuru Jiang1 aYu Wu1 aYuyao Zhang00aClassifying Wu-Qing Couplets and General Couplets with Structural and Semantic Features  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139026338&doi=10.1007%2f978-3-030-81197-6_48&partnerID=40&md5=74a6158ace1dff7f54ed7cceb63561f0  a562-5750 v12278 LNAI3 aThe couplet custom is one of China’s intangible cultural heritage, and it occupies a pivotal position in Chinese traditional culture. The general couplet requires that the first sentence and the second sentence be opposite and semantically coherent. As a particular type of couplets, Wu-Qing couplets are related on the character level but irrelevant at the sentence level. Such characteristics make the Wu-Qing couplet significantly different in structure and semantics compared to the general couplets. In this paper, Convolutional Neural Network is used to extract structural features. Long Short-Term Memory network and vector operations are used to extract semantic features, and the Attention mechanism is used to strengthen structural and semantic information. Finally, we propose a model with structural and semantic features between general couplets and Wu-Qing couplets. The final F1 score reached 82.6, which was an increase of 5.4 compared to the baseline model.  a03029743 (ISSN); 9783030811969 (ISBN)