TAILIEUCHUNG - Báo cáo khoa học: "Co-Training for Cross-Lingual Sentiment Classification"

The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine translation services are used for eliminating the language gap between the training set and test set, and English features and Chinese features are considered as two independent views of the classification problem. . | Co-Training for Cross-Lingual Sentiment Classification Xiaojun Wan Institute of Compute Science and Technology Key Laboratory of Computational Linguistics MOE Peking University Beijing 100871 China wanxiaojun@ Abstract The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine translation services are used for eliminating the language gap between the training set and test set and English features and Chinese features are considered as two independent views of the classification problem. We propose a cotraining approach to making use of unlabeled Chinese data. Experimental results show the effectiveness of the proposed approach which can outperform the standard inductive classifiers and the transductive classifiers. 1 Introduction Sentiment classification is the task of identifying the sentiment polarity of a given text. The sentiment polarity is usually positive or negative and the text genre is usually product review. In recent years sentiment classification has drawn much attention in the NLP field and it has many useful applications such as opinion mining and summarization Liu et al. 2005 Ku et al. 2006 Titov and McDonald 2008 . To date a variety of corpus-based methods have been developed for sentiment classification. The methods usually rely heavily on an annotated corpus for training the sentiment classifier. The sentiment corpora are considered as the most valuable resources for the sentiment classification task. However such resources in different languages are very imbalanced. Because most previous work focuses on English sentiment classification many annotated corpora for English sentiment classification are .

• Báo cáo khoa học
• Co Training for Cross Lingual
• Sentiment Classification
• Xiaojun Wan
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học