Autor:	Dezhou Shen
Rok vydání:	2021
Předmět:	Computer science Chinese word Arithmetic Transformer (machine learning model) Sequence (medicine)
DOI:	10.21203/rs.3.rs-149338/v1
Popis:	Chinese word segment is widely studied in document analysis. The accuracy of the current popular word segment model, LSTM+CRF, is still not satisfactory. Models trained by the popular dataset often fails in the out-domain situation. In this paper, combining the Transformer-XL layer, the Fully-Connect layer, and the Conditional Random Field layer, the proposed model improved 3.23% in the macro-F1 score, comparing to the BERT+CRF model, on the MSR2005 Chinese word segment test dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::eb502f32c55fad9455ed220af39c4fbd https://doi.org/10.21203/rs.3.rs-149338/v1 Zobrazit plný text záznamu