A Web-based Unsupervised Algorithm for Learning Transliteration Model to Improve Translation of Low-Frequency Proper Names

Shia, Min-Shiang; Lin, Jiun-Hung; Yu, Scott; Lu, Wen-Hsiang

doi:10.1109/nlpke.2005.1598774

Cited by 4 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The major approaches of NER for Chinese historical texts have been focused on handcrafted heuristic rules [6][7][8][9][10][11] to formulate entity features from context-derived patterns in a quick manner, which depend a lot on relevant domain knowledge. For example, MARKUS 3 [12] is a well-known online platform for automatically tagging a range of historical named entities (personal names, place names and bureaucratic offices, etc.)…”

Section: A Entity Extraction Of Chinese Historical Textsmentioning

confidence: 99%

MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical Texts

Yan

Su²,

Wang³

2020

IEEE Access

View full text Add to dashboard Cite

Named Entity Recognition (NER) systems have been largely advanced by deep neural networks in the recent decade. However, the state-of-the-arts on NER have been less applied to Chinese historical texts due to the lack of standard corpora in Chinese historical domains and the difficulty of accessing a quality ancient corpus. This paper addresses the respective issues and proposes an efficient automatic processing solution for tackling NER of ancient Chinese data, including the implementation of data-driven tagging and an innovative end-to-end network namely "MoGCN" (Mixture of Gated Convolutional Neural Network). A corpus consisting of three genres of Chinese historical classics is generated by our tagging approach, which is experimented for uncovering the generalization ability of proposed model. The empirical analysis demonstrates that our proposed model achieves the best results with above 1.5% F1-score improvement over other sophisticated models in this dataset, where the experimental performance shows positive dependence on the quality of corpus. Furthermore, our model can perform much better on shorter entities especially for 2-charater ones, while many long-range entities can be only identified by our model based on our auxiliary attribute analysis. This work serves as a preliminary exploitation of NER for historical data, providing unique insights and reference values for similar tasks. Future work should be focused on more exploration about NER optimization on massive Chinese traditional texts with linguistic features and learning strategies. INDEX TERMS Named entity recognition, gated neural network, Chinese historical texts

show abstract

Section: A Entity Extraction Of Chinese Historical Textsmentioning

confidence: 99%