Learning Improved Class Vector for Multi-Class Question Type Classification

Recent research in NLP has exploited word embedding to achieve outstanding results in various tasks such as; spam filtering, text classification and summarization and others. Present word embedding algorithms have power to capture semantic and syntactic knowledge about word, but not enough to portray the distinct meaning of polysemy word. Many work has utilized sense embeddings to integrate all possible meaning to word vector, which is computationally expensive. Context embedding is another way out to identify word’s actual meaning, but it is hard to enumerate every context with a small size dataset. This paper has proposed a methodology to generate improved class-specific word vector that enhance the distinctive property of word in a class to tackle light polysemy problem in question classification. The proposed approach is compared with baseline approaches, tested using deep learning models upon TREC, Kaggle and Yahoo questions datasets and respectively attain 93.6%, 91.8% and 89.2% accuracy.