@InProceedings{JerônimoCampBapt:2015:MiInTe,
               author = "Jer{\^o}nimo, Caio Lib{\^a}nio Melo and Campelo, Cl{\'a}udio E. 
                         C. and Baptista, Cl{\'a}udio de Souza",
          affiliation = "{Universidade Federal de Campina Grande (UFCG)} and {Universidade 
                         Federal de Campina Grande (UFCG)} and {Universidade Federal de 
                         Campina Grande (UFCG)}",
                title = "Mining influential terms for toponym recognition and resolution",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Fileto, Renato and Korting, Thales Sehn",
                pages = "143--154",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 16. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The detection of toponyms present in text has appeared as a useful 
                         resource for many different applications, such as for social 
                         network analysers and for geographic search engines. The variety 
                         of ambiguities present in the geoparsing process represents one of 
                         the main challenges related to the process of detecting toponyms, 
                         bringing the need for treating this problem with careful 
                         attention. One important technique to detect toponyms is based on 
                         the presence of influential terms, which are terms that could 
                         indicate the existence of geographical references in the text. 
                         This paper presents an approach to automatically identifying 
                         relevant influential terms for a given language, as well as a set 
                         of attributes relating these terms with toponyms. The technique 
                         presented here was validated with an existing geoparser, using a 
                         training set based on online news. The results indicate the 
                         technique is effective in identifying influential terms, and has 
                         shown that the geoparsers capabilities of detecting toponyms have 
                         improved by using the generated list of influential terms.",
  conference-location = "Campos do Jord{\~a}o",
      conference-year = "27 nov. a 02 dez. 2015",
                 issn = "2179-4820",
             language = "en",
                  ibi = "8JMKD3MGPDW34P/3KP35BL",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3KP35BL",
           targetfile = "proceedings2015_p14.pdf",
        urlaccessdate = "27 abr. 2024"
}