Fast and Accurate Part{of{speech Tagging: the Svm Approach Revisited

By Jes Us Gim Enez And Llu. 2003

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Devamanyu Hazarika
2. Soujanya Poria
3. Erik Cambria
4. Tom Young
5. Jin-Dong Kim
6. Navonil Majumder
7. Tomoko Ohta
8. Junichi Tsujii

Citations in the corpus

• Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “Giménez and Marquez [132] employed oneagainst-all SVM based on manually-defined features within a seven-word window, in which some basic n-gram patterns were evaluated to form binary features such as: “previous word is the”, ”two preceding tags are DT NN”, etc.”
• Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “giMéNEz AND MARquEz [125] SvM WiTH MANuAL fEATuRE PATTERN 97.”
• The effects of part–of–speech tagging on text–to–speech synthesis for resource–scarce languages (2010, methodology): “600s, respectively (the difference in speed has also been noted in [32]).”
• Developing a Robust Part-of-Speech Tagger for Biomedical Text (2005, background): “On the same sets for training and testing, Gimenez and Marquez [9] achieved an accuracy of 97.05% with support vector machines and Collins [10] achieved an accuracy of 97.11% with a discriminative HMM model.”