Indonesian-English Cross Language Question Answering

Our Indonesian-English Cross Language Question Answering (CLQA) is divided into 4 components: question analyzer, keyword translator, passage retriever and answer finder component. The Indonesian question is inputted into a question analyzer which yields Indonesian keyword list, Indonesian question focus and question class. We defined the question class by using an SVM machine implemented in Weka[15]. Because Indonesian is a poor data resource language, we use a bigram frequency feature as an addition feature for the question classification. The Indonesian keywords are translated into English using an Indonesian-English bilingual dictionary. The English translations are composed into a boolean query to retrieve relevant passages. We select the passages within 3 highest IDF scores. In the answer finder, the answer is located by using an SVM method for text chunking implemented in Yamcha[4]. Different with other Indonesian-English CLQA[1,14], we do not tag the name entities in the target documents, instead we only do the POS tagging by using TreeTagger[12] for the target documents. Based on our experiment in Indonesian QA, we choose to use question class, question features and document features for the machine learning based answer finder. We also complement the WordNet distance feature for the document features. By using 284 questions as the test data, we achieved about 31.69% accuracy on top 5 answers which is better than other IndonesianEnglish CLQAs.