Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents the first attempt to apply instance-level transfer learning technique to#N# cross lingual opinion analysis by using the translation of annotated corpus from other languages as the#N# supplementary training data for the opinion classifier for target language. Firstly, Transfer AdaBoost algorithm (TrAdaBoost) is applied as the base transfer learning strategy, which makes use of few labeled#N# examples in target language to leverage the large annotated corpus in other languages and selects the#N# translated annotated data with good confidence as supplementary training samples to improve the opinion classifier for target language. Considering that the re-ighting scheme adopted in TrAdaBoost has#N# the potential risk of over-discarding of source training examples, this algorithm is further improved by#N# combining the bagging procedure and the boosting procedure of TrAdaBoost, named Transfer Boosting#N# with Bagging (TrBB). The proposed two algorithms are evaluated on document-level and sentence-level#N# Chinese opinion analysis, respectively. The achieved encouraging performances show that the proposed#N# transfer learning based approaches improve the opinion analysis effectively by exploiting small training#N# data in target language and large cross lingual training data.
['Jun Xu', 'Ruifeng Xu', 'Yuxin Ding', 'Xiaolong Wang', 'Chunyu Kit']
I am writing a research paper on a idea Cross Lingual Opinion Analysis via Transfer Learning
[{"from": "human", "value": "I am writing a research paper on a idea Cross Lingual Opinion Analysis via Transfer Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents the first attempt to apply instance-level transfer learning technique to#N# cross lingual opinion analysis by using the translation of annotated corpus from other languages as the#N# supplementary training data for the opinion classifier for target language. Firstly, Transfer AdaBoost algorithm (TrAdaBoost) is applied as the base transfer learning strategy, which makes use of few labeled#N# examples in target language to leverage the large annotated corpus in other languages and selects the#N# translated annotated data with good confidence as supplementary training samples to improve the opinion classifier for target language. Considering that the re-ighting scheme adopted in TrAdaBoost has#N# the potential risk of over-discarding of source training examples, this algorithm is further improved by#N# combining the bagging procedure and the boosting procedure of TrAdaBoost, named Transfer Boosting#N# with Bagging (TrBB). The proposed two algorithms are evaluated on document-level and sentence-level#N# Chinese opinion analysis, respectively. The achieved encouraging performances show that the proposed#N# transfer learning based approaches improve the opinion analysis effectively by exploiting small training#N# data in target language and large cross lingual training data."}]