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 par- ticular set of entities found
during training.

我们提出了一种新方法用于识别目标化命名实体；我们的方法不需要重新训练统计模型，这使得它对于经常更新的实体类型更加适应。我们的系统使用从 Twitter 收集的数据，以电影标题为例进行测试。在两个评估集上进行测试，我们的最终模型显示 F1 得分为 76.19％和 78.70％，这表明我们的方法完全不偏向于任何训练过程中找到的特定实体集。