@article{
 title = {Named Entity Extraction from Text Messages},
 type = {article},
 year = {1998},
 pages = {287-292},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.27.3021&rep=rep1&type=pdf},
 publisher = {Citeseer},
 institution = {Lund University},
 id = {02e404df-c313-3841-a96d-7341a3ce8e8e},
 created = {2011-12-29T19:53:53.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {named entities},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Kubala1998},
 private_publication = {false},
 abstract = {The primary goal of this Master's thesis was to investigate the possibility to have a viable named entity recognition (NER) system for cellular text messages that would be appropriate for commercial use. Most NER system deals with text that is structured, formal, well written, with a good grammatical structure, and with few spelling errors. these qualities and have instead a short handed language, mixed language, and emoticons. We wanted to examine if it was possible to incorporate the techniques and tools required into a cellular telephone and to have the system analyze the incoming text messages within a reasonable response time. The named entity focus was on locations, names, dates, times, and telephone numbers with the idea that extraction of these entities could be utilized by other applications such as meeting details for calendar, location look-up via Google map, or contact information for the cellular telephone's record of contacts. We visualized the results using the short message service (SMS) application which, if the user chooses to, can send the extracted information to the appropriate application. We reached an F-score of 86% for strict matches and 89% for partial matches.},
 bibtype = {article},
 author = {Kubala, Francis and Schwartz, Richard and Stone, Rebecca and Weischedel, Ralph},
 journal = {Context}
}

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