---
abstract: 'We present a novel approach for recognizing what we call targetable named entities; that is, named entities in a targeted set (e.g, movies, books, TV shows). Unlike many other NER systems that need to retrain their statistical models as new entities arrive, our approach does not require such retraining, which makes it more adaptable for types of entities that are frequently updated. For this preliminary study, we focus on one entity type, movie title, using data collected from Twitter. Our system is tested on two evaluation sets, one including only entities corresponding to movies in our training set, and the other excluding any of those entities. Our final model shows F1-scores of 76.19% and 78.70% on these evaluation sets, which gives strong evidence that our approach is completely unbiased to any particular set of entities found during training.'
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creators_id:
  - asandee@amazon.com
  - choi@mathcs.emory.edu
creators_name:
  - family: Ashwini
    given: Sandeep
    honourific: ''
    lineage: ''
  - family: Choi
    given: Jinho D.
    honourific: ''
    lineage: ''
date: 2014-07-31
date_type: completed
datestamp: 2014-08-24 20:59:40
department: Mathematics and Computer Science
dir: disk0/00/00/97/64
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eprintid: 9764
fileinfo: /9764/1.hassmallThumbnailVersion/cogprints2014a.pdf;/9764/1/cogprints2014a.pdf
full_text_status: public
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institution: Emory University
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ispublished: unpub
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lastmod: 2015-04-20 11:40:32
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reportno: ~
rev_number: 24
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status_changed: 2014-08-24 20:59:40
subjects:
  - comp-sci-lang
  - ling-compara
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title: Targetable Named Entity Recognition in Social Media
type: preprint
userid: 24376
volume: ~
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