Identifying Real or Fake Articles: Towards better Language Modeling

The problem of identifying good features for improving conventional language models like trigrams is presented as a classification task in this paper. The idea is to use various syntactic and semantic features extracted from a language for classifying between real-world articles and articles generated by sampling a trigram language model. In doing so, a good accuracy obtained on the classification task implies that the extracted features capture those aspects of the language that a trigram model may not. Such features can be used to improve the existing trigram language models. We describe the results of our experiments on the classification task performed on a Broadcast News Corpus and discuss their effects on language modeling in general.