Abstract
Detecting the location entities mentioned in Twitter messages is useful in text mining for business, marketing or defence applications. Therefore, techniques for extracting the location entities from the Twitter textual content are needed. In this work, we approach this task in a similar manner to the Named Entity Recognition (NER) task focused only on locations, but we address a deeper task: classifying the detected locations into names of cities, provinces/states, and countries. We approach the task in a novel way, consisting in two stages. In the first stage, we train Conditional Random Fields (CRF) models with various sets of features; we collected and annotated our own dataset or training and testing. In the second stage, we resolve cases when there exist more than one place with the same name. We propose a set of heuristics for choosing the correct physical location in these cases. We report good evaluation results for both tasks.
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Inkpen, D., Liu, J., Farzindar, A., Kazemi, F., Ghazi, D. (2015). Detecting and Disambiguating Locations Mentioned in Twitter Messages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_24
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DOI: https://doi.org/10.1007/978-3-319-18117-2_24
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