کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
391497 661845 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
ترجمه فارسی عنوان
ترکیبی از یادگیری فعال و خودآموزی برای طبقه بندی احساسات متقابل و تجزیه و تحلیل چگالی نمونه های بدون برچسب
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• We combine active learning and self-training for cross-lingual sentiment classification.
• Density analysis of unlabelled data is used to select representative examples in active learning.
• We test our proposed model on three different target languages.
• Results show that incorporating density analysis can speed up learning process.
• Results show that combination of two approaches outperforms each individual method.

In recent years, research in sentiment classification has received considerable attention by natural language processing researchers. Annotated sentiment corpora are the most important resources used in sentiment classification. However, since most recent research works in this field have focused on the English language, there are accordingly not enough annotated sentiment resources in other languages. Manual construction of reliable annotated sentiment corpora for a new language is a labour-intensive and time-consuming task. Projection of sentiment corpus from one language into another language is a natural solution used in cross-lingual sentiment classification. Automatic machine translation services are the most commonly tools used to directly project information from one language into another. However, since term distribution across languages may be different due to variations in linguistic terms and writing styles, cross-lingual methods cannot reach the performance of monolingual methods. In this paper, a novel learning model is proposed based on the combination of uncertainty-based active learning and semi-supervised self-training approaches to incorporate unlabelled sentiment documents from the target language in order to improve the performance of cross-lingual methods. Further, in this model, the density measures of unlabelled examples are considered in active learning part in order to avoid outlier selection. The empirical evaluation on book review datasets in three different languages shows that the proposed model can significantly improve the performance of cross-lingual sentiment classification in comparison with other existing and baseline methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 317, 1 October 2015, Pages 67–77
نویسندگان
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