Social media texts are significant information sources for several
application areas including trend analysis, event monitoring, and opinion
mining. Unfortunately, existing solutions for tasks such as named entity
recognition that perform well on formal texts usually perform poorly when
applied to social media texts. In this paper, we report on experiments that
have the purpose of improving named entity recognition on Turkish tweets, using
two different annotated data sets. In these experiments, starting with a
baseline named entity recognition system, we adapt its recognition rules and
resources to better fit Twitter language by relaxing its capitalization
constraint and by diacritics-based expansion of its lexical resources, and we
employ a simplistic normalization scheme on tweets to observe the effects of
these on the overall named entity recognition performance on Turkish tweets.
The evaluation results of the system with these different settings are provided
with discussions of these results.

本文研究了基于社交媒体文本的命名实体识别，旨在提高土耳其推特上的识别效果，通过放宽语言规则和扩展词汇资源，以及使用简单的标准化方案，得到了实验结果和讨论。