A Deep Network Model for Paraphrase Detection in Short Text Messages
Table 4: Comparison with state-of-the-art of results on SemEval 2015 Twitter dataset.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
Firstly, we compare the results of the proposed approach with the best methods on clean text Microsoft Paraphrase dataset, and then with the state-of-the-art methods on noisy Twitter dataset. Their model uses WordNet, OntoNotes, Wiktionary, and the Brown corpus. Their approach performed quite well on MSRP dataset, but provide worse results on Twitter dataset. However, it can be seen from the results presented in Table As can be observed, the results from this comparison, our approach outperforms all related methods with respect to the F1-score. The main reason for this is that our approach leverages the semantic information at both coarse-grained sentence-level features and fine-grained word-level features for detecting paraphrases on tweets. The ensemble-based method proposed by Zarrella et al. F1-score.