Syllables for Sentence Classification in Morphologically Rich Languages
Madhuri Tummalapalli, Radhika Mamidi · Institutional Repositories DataBase (IRDB) · 2018
Sentence Classification is one of the most fundamental tasks in NLP, where the aim is to classify a given sentence into a pre-defined set of classes.A lot of work has been done in English in the last few years, which vary in their methodologies.A huge proportion of these works represent the input sentences as a sequence of words in their models.Only a few of them rely on character level representation.Through this work, we introduce a new method for representing a sentence -as a sequence of syllables.As we show in this work, syllables are a better choice to represent the sub-word level information in a sentence, which is essential for morphologically rich languages.We consider the tasks of Sentiment Analysis and Question Classification in three languages showing varied morphological richness -English, Hindi and Telugu.Through extensive evaluation, we show that syllables are the best performing input type when compared to words or characters for the morphologically rich languages -Hindi and Telugu.