Using paraphrases to improve tweet classification: Comparing WordNet and word embedding approaches

@article{Li2016UsingPT,
  title={Using paraphrases to improve tweet classification: Comparing WordNet and word embedding approaches},
  author={Quanzhi Li and Sameena Shah and Mohammad Mahdi Ghassemi and Rui Fang and Armineh Nourbakhsh and Xiaomo Liu},
  journal={2016 IEEE International Conference on Big Data (Big Data)},
  year={2016},
  pages={4014-4016}
}
Two of the major problems in social media message classification are the data sparseness issue and the high degree of lexical variation. Paraphrases, or synonyms, are alternative ways of expressing the same meaning using different lexical variations. In this study, we try to use paraphrases to improve tweet topic classification performance. We explored two… CONTINUE READING