Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss part-of-speech (POS) tagging in presence of large, fine-grained label sets using conditional random fields (CRFs).  propose improving tagging accuracy by utilizing dependencies within sub-components of the fine-grained labels. These sub-label dependencies are incorporated into the CRF model via a (relatively) straightforward feature extraction scheme. Experiments on five languages show that the approach can yield significant improvement in tagging accuracy in case the labels have sufficiently rich inner structure.
['Miikka Silfverberg', 'Teemu Ruokolainen', 'Krister Linden', 'Mikko Kurimo']
I am writing a research paper on a idea Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub-Label Dependencies for Improved Accuracy
[{"from": "human", "value": "I am writing a research paper on a idea Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub-Label Dependencies for Improved Accuracy"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss part-of-speech (POS) tagging in presence of large, fine-grained label sets using conditional random fields (CRFs).  propose improving tagging accuracy by utilizing dependencies within sub-components of the fine-grained labels. These sub-label dependencies are incorporated into the CRF model via a (relatively) straightforward feature extraction scheme. Experiments on five languages show that the approach can yield significant improvement in tagging accuracy in case the labels have sufficiently rich inner structure."}]