Abstract
In order to process tweet segmentation, it requires a sophisticated framework. Through a specific process the tweets are segmented based on several aspects. Some of them are the linguistics or context knowledge based and this is so protective as well as effective. They are separated by the downstream applications. It finds the best segmentation of a tweet by increasing the sum of the viscousness scores of its candidate segments. For testing planned system, we tend to thought-about two tweet phase datasets, the experimental results by the planned system considerably improved in terms of global and local contexts.
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References
Sil, Yates, A.: Re-ranking for joint named-entity recognition and linking. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2369–237 (2013)
Liu,K.-L., Li, W.-J., Guo, M.: Emoticon smoothed language models for twitter sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1678–1684 (2012)
Narmadha, R.P., Sreeja, G.G.: A survey on online tweet segmentation for linguistic features. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6, 7 January (2016). IEEE
Li, C., Weng, J., He, Q., Yao, Y., Datta, A., Sun, A., Lee, B.-S.: Twiner: named entity recognition in targeted twitter stream. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development Information Retrieval, pp. 721–730 (2012)
Li, C., Sun, A., Weng, J., He, Q.: Exploiting hybrid contexts for tweet segmentation. In: Proceeding of the 36th International ACM SIGIR Conference on Research and Development Information Retrieval, pp. 523–532 (2013)
Ritter, A., Clark, S., Mausam, Etzioni, O.: Named entity recognition in tweets: an experimental study. In Proceedings of the Conference on Empirical Methods Natural Language Processing, pp. 1524–1534 (2011)
Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 359–367 (2011)
Liu, X., Zhou, Z., Fu, Wei, F., Zhou, M.: Exacting social events for tweets using a factor graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1692–1698 (2012)
Liao, Z., Song, Y., Huang, L., He, L.W., He, Q.: Task trail: an effective segmentation of user search behavior. IEEE Trans. Knowl. Data Eng. 1(1), 1– (2014, December)
Karatay, D., Karagoz, P.: User interest modeling in twitter with named entity recognition. In: 5th Workshop on Making Sense of Microposts 2015 May 18 (2015)
Yamada, I., Takeda, H., Takefuji, Y.: Enhancing named entity recognition in twitter messages using entity linking. In: Proceedings of the Workshop on Noisy User-generated Text 2015, pp. 136–140 (2015)
Chang, Y., Wang, X., Mei, Q., Liu, Y.: Towards Twitter context summarization with user influence models. In: Proceedings of the sixth ACM international conference on Web search and data mining 2013 Feb 4, pp. 527–536 (2013). ACM
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Someswara Rao, C., Shiva Shankar, R., Appaji, S.V. (2020). Tweet Segmentation—A Novel Mechanism. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 160. Springer, Singapore. https://doi.org/10.1007/978-981-32-9690-9_52
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DOI: https://doi.org/10.1007/978-981-32-9690-9_52
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