Classifying Vietnamese Disease Outbreak Reports with Important Sentences and Rich Features
This work addresses text classification for biosurveillance in Vietnamese, but it is incremental as it builds on existing methods with minor gains.
The paper tackled the problem of classifying Vietnamese disease outbreak reports by identifying important sentences and rich features, achieving an F-score of 86.67%, a 0.38% improvement over using all raw text.
Text classification is an important field of research from mid 90s up to now. It has many applications, one of them is in Web-based biosurveillance systems which identify and summarize online disease outbreak reports. In this paper we focus on classifying Vietnamese disease outbreak reports. We investigate important properties of disease outbreak reports, e.g., sentences containing names of outbreak disease, locations. Evaluation on 10-time 10- fold cross-validation using the Support Vector Machine algorithm shows that using sentences containing disease outbreak names with its preceding/following sentences in combination with location features achieve the best F-score with 86.67% - an improvement of 0.38% in comparison to using all raw text. Our results suggest that using important sentences and rich feature can improve performance of Vietnamese disease outbreak text classification.