Location detection and disambiguation from twitter messages

Inkpen, Diana; Ji, Liu; Farzindar, Atefeh; Kazemi, Farzaneh; Ghazi, Diman

doi:10.1007/s10844-017-0458-3

Cited by 36 publications

(34 citation statements)

References 9 publications

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“…Such geographic distributions can be utilized for disambiguating a target place name based on its context words. Inkpen et al (2015) used both a gazetteer and word features (e.g., part of speech, left words, and right words) to train a conditional random field model which can extract cities, states, and countries from texts.…”

Section: Related Workmentioning

confidence: 99%

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Mao

McKenzie

2018

International Journal of Geographical Information Science

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Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g., the entire world), recent establishment (e.g., the name of a newly-opened shopping center), or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements, and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates, and performs multi-scale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.

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Section: Related Workmentioning

confidence: 99%

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Mao

McKenzie

2018

International Journal of Geographical Information Science

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“…This information is presented to the users to verify matching pairs of entities. (Inkpen et al, 2017) proposed a set of heuristic rules to disambiguate location names in Twitter 5 messages. Their heuristics rely on geographical (latitude-longitude, geographic hierarchy) and demographic information (population of a region).…”

Section: Introductionmentioning

confidence: 99%

Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data

Maheshwari¹,

Kumar²,

Ramakrishnan³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: De

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We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. Scarce, unstructured information poses a challenge to Entity Resolution(ER) and snippet ranking. Additionally, because the same set of entities may be associated with multiple locations, Location Disambiguation(LD) is a problem. The mentions and descriptions of temples 1 exist in the order of hundreds of thousands, with such data generated by various users in various forms such as text (Wikipedia pages), videos (YouTube videos), blogs, etc. We demonstrate an integrated approach using a combination of grammar rules for parsing and unsupervised (clustering) algorithms to resolve entity and locations with high confidence. A demo of our system is accessible at tinyurl.com/templedemos 2 .Our system is open source and available on GitHub 3 .

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“…Inkpen et al [6] developed an algorithm that extracts expressions composed of one or more words for each place name. They use a Conditional Random Fields classifier, which is based on an unguided graphical model that is used for unstructured predictions [4].…”

Section: Related Workmentioning

confidence: 99%

“…As well as we know, only two works take into account disambiguation: Silva et al [15] and Inkpen et al [6]. The first one recognizes if ambiguity is present, so it takes into account the elements related to involved entities, however it does not take into account variations in the names of places.…”

Section: Related Workmentioning

confidence: 99%

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Georeference Assignment of Locations based on Context

Reyes-Palacios¹,

Aldana-Bobadilla²,

López-Arévalo³

et al.

Kalpa Publications in Computing

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Modern Text Mining techniques seek for extract information in useful formats such as georeferences in digital documents. Automatic recognition of location names in texts is usually solved through Named Entity Recognition (NER) systems. Most current NER are based on Machine Learning and have very high accuracy in detection of location entities in digital documents, especially if the texts are in English due to the lack of available an- notated corpora in other languages. However, recent studies are dealing with the challenge of taking the output labels of a NER system and then gather, from a gazetteer, their exact unambiguous geographical coordinates. This is challenging mainly because toponyms use to be very ambiguous, so research in disambiguation methods is relevant. In this paper we describe some of the main ideas towards a method to associate locations with geographical data removing possible confusion between entities with the same name. So far, we have already accomplished Geographic NER and coordinates retrieval but the main research is still in course. We largely discuss about the state of the art around Geoparsing; we explain how our Geographic Entity Recognition module works and finally we describe the research proposal focusing in ambiguity detection.

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Location detection and disambiguation from twitter messages

Cited by 36 publications

References 9 publications

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data

Georeference Assignment of Locations based on Context

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