Named entity recognition on real data: A preliminary investigation for Turkish

Named Entity Recognition (NER) is a well-studied area in natural language processing (NLP) and the reported results in the literature are generally very high (~>%95) for most of the languages. Today, the focus area of most practical natural language applications (i.e. web mining, sentiment analysis, machine translation) is real natural language data such as Web2.0 or speech data. Nevertheless, the NER task is rarely investigated on this type of data which differs severely from formal written text. In this paper, we present 3 new Turkish data sets from different domains (on this focused area; namely from Twitter, a Speech-to-Text Interface and a Hardware Forum) annotated specifically for NER and report our first results on them. We believe, the paper draws light to the difficulty of these new domains for NER and the possible future work.