  Recently, due to the increasing popularity of social media, the necessity for
extracting information from informal text types, such as microblog texts, has
gained significant attention. In this study, we focused on the Named Entity
Recognition (NER) problem on informal text types for Turkish. We utilized a
semi-supervised learning approach based on neural networks. We applied a fast
unsupervised method for learning continuous representations of words in vector
space. We made use of these obtained word embeddings, together with language
independent features that are engineered to work better on informal text types,
for generating a Turkish NER system on microblog texts. We evaluated our
Turkish NER system on Twitter messages and achieved better F-score performances
than the published results of previously proposed NER systems on Turkish
tweets. Since we did not employ any language dependent features, we believe
that our method can be easily adapted to microblog texts in other
morphologically rich languages.
