Abstract
The automatic identification of location expressions in social media text is an actively researched task. We present a novel approach to detection mentions of locations in the texts of microblogs and social media. We propose an approach based on Noun Phrase extraction and n-gram based matching instead of the traditional methods using Named Entity Recognition (NER) or Conditional Random Fields (CRF), arguing that our method is better suited to noisy microblog text. Our proposed system is comprised of several individual modules to detect addresses, Points of Interest (e.g. hospitals or universities), distance and direction markers; and location names (e.g. suburbs or countries). Our system won the ALTA 2014 Twitter Location Detection shared task with an F-score of 0.792 for detecting location expressions in a test set of 1,000 tweets, demonstrating its efficacy for this task. A number of directions for future work are discussed.
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Notes
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Requests for the data should also be directed to the authors of [5].
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Available for download free of charge under a creative commons attribution license.
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A noun phrase that contains other NPs, for example, within prepositions.
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The web service offers a number of advanced features that can help increase search specificity.
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See [4] for more details about these metrics.
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Acknowledgments
We would like to thank our three anonymous reviewers for their valuable comments. The data and the task’s original idea is from John Lingad’s Honours project (The University of Sydney) co-supervised with Jie Yin (CSIRO). The shared task prize was sponsored by IBM Research.
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Malmasi, S., Dras, M. (2016). Location Mention Detection in Tweets and Microblogs. In: Hasida, K., Purwarianti, A. (eds) Computational Linguistics. PACLING 2015. Communications in Computer and Information Science, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-10-0515-2_9
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