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2018 | OriginalPaper | Buchkapitel

Mining POI Alias from Microblog Conversations

verfasst von : Yihong Zhang, Lina Yao

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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Abstract

In location-based analysis for microblogs, it is important to know if two toponyms refer to the same point-of-interest, i.e., alias. However, existing online knowledge bases are often incomplete or inaccurate for toponym alias data, especially for those used in informal conversations. In this paper, we propose a method for extracting compatible toponyms from microblog conversations. We first extract a number of coordinate-associated toponyms, then use compatibility measures to identify compatible toponyms. We propose three compatibility measures, namely, geographical closeness, surface name similarity, and association similarity. We show that by combining these measures and using particle swarm optimization for weight tuning, we can reach a high matching accuracy. The finding of this paper can be useful for improving location-based analysis as well as extending existing knowledge bases.

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http://www.geonames.org/.

Calculation can be found at http://www.movable-type.co.uk/scripts/latlong.html.

An algorithm for calculating edit distance can be found in https://nlp.stanford.edu/IR-book/html/htmledition/edit-distance-1.html.

https://dev.twitter.com/streaming/reference/get/statuses/sample.

https://dev.twitter.com/rest/reference/get/statuses/user_timeline.

If a user has posted less than 1,000 tweets, we collect all past tweets.

Abdelhaq, H., Sengstock, C., Gertz, M.: EvenTweet: online localized event detection from Twitter. Proc. VLDB Endow. 6(12), 1326–1329 (2013)CrossRef

Bollegala, D., Matsuo, Y., Ishizuka, M.: Automatic discovery of personal name aliases from the web. IEEE Trans. Knowl. Data Eng. 23(6), 831–844 (2011)CrossRef

Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating Twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 759–768 (2010)

Dredze, M., Paul, M.J., Bergsma, S., Tran, H.: Carmen: a Twitter geolocation system with applications to public health. In: AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI, pp. 20–24 (2013)

Gelernter, J., Balaji, S.: An algorithm for local geoparsing of microtext. GeoInformatica 17(4), 635–667 (2013)CrossRef

Graham, M., Hale, S.A., Gaffney, D.: Where in the world are you? Geolocation and language identification in Twitter. Prof. Geogr. 66(4), 568–578 (2014)CrossRef

Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765–774. ACM (2011)

Hoffart, J., Altun, Y., Weikum, G.: Discovering emerging entities with ambiguous names. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 385–396. ACM (2014)

Hsiung, P., Moore, A., Neill, D., Schneider, J.: Alias detection in link data sets. In: Proceedings of the International Conference on Intelligence Analysis, vol. 4 (2005)

10.

Huang, H., Wen, Z., Yu, D., Ji, H., Sun, Y., Han, J., Li, H.: Resolving entity morphs in censored data. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1083–1093 (2013)

11.

Ikawa, Y., Enoki, M., Tatsubori, M.: Location inference using microblog messages. In: Proceedings of the 21st International World Wide Web Conference Companion, pp. 687–690 (2012)

12.

Ji, Z., Sun, A., Cong, G., Han, J.: Joint recognition and linking of fine-grained locations from tweets. In: Proceedings of the 25th International Conference on World Wide Web, pp. 1271–1281 (2016)

13.

Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)

14.

Li, C., Sun, A.: Fine-grained location extraction from tweets with temporal awareness. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 43–52 (2014)

15.

Li, C., Sun, A., Weng, J., He, Q.: Tweet segmentation and its application to named entity recognition. IEEE Trans. Knowl. Data Eng. 27(2), 558–570 (2015)CrossRef

16.

Li, R., Lei, K.H., Khadiwala, R., Chang, K.-C.: TEDAS: a Twitter-based event detection and analysis system. In: Proceedings of 28th International Conference on Data Engineering, pp. 1273–1276 (2012)

17.

Lingad, J., Karimi, S., Yin, J.: Location extraction from disaster-related microblogs. In: Proceedings of the 22nd International World Wide Web Conference Companion, pp. 1017–1020 (2013)

18.

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, vol. 1, pp. 359–367. Association for Computational Linguistics (2011)

19.

Lucia, W., Ferrari, E.: Egocentric: ego networks for knowledge-based short text classification. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 1079–1088. ACM (2014)

20.

Malmasi, S., Dras, M.: Location mention detection in tweets and microblogs. In: Hasida, K., Purwarianti, A. (eds.) Computational Linguistics. CCIS, vol. 593, pp. 123–134. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0515-2_9CrossRef

21.

Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International World Wide Web Conference, pp. 851–860 (2010)

22.

Schulz, A., Hadjakos, A., Paulheim, H., Nachtwey, J., Mühlhäuser, M.: A multi-indicator approach for geolocalization of tweets. In: Proceedings of the Seventh International Conference on Weblogs and Social Media, pp. 573–582 (2013)

23.

Zhang, W., Gelernter, J.: Geocoding location expressions in Twitter messages: a preference learning method. J. Spat. Inf. Sci. 2014(9), 37–70 (2014)

24.

Zhang, Y., Szabo, C., Sheng, Q.Z.: Sense and focus: towards effective location inference and event detection on Twitter. In: Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9418, pp. 463–477. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26190-4_31CrossRef

Titel: Mining POI Alias from Microblog Conversations
verfasst von: Yihong Zhang
Lina Yao
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93033-6

Electronic ISBN: 978-3-319-93034-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93034-3_34

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