Abstract
Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0:8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection F1-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases.- Anthology ID:
- C16-1271
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2880–2890
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/C16-1271/
- DOI:
- Cite (ACL):
- Kuntal Dey, Ritvik Shrivastava, and Saroj Kaushik. 2016. A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2880–2890, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs (Dey et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/C16-1271.pdf