The potential of processing user-generated texts freely available on the web is widely recognized, but due to the non-canonical nature of the language used in the web, it is not possible to process these data using conventional methodologies designed for well-edited formal texts. Procedures for properly annotating raw web data have not been as extensively researched as those for annotating well-edited texts, as also evident from the viewpoint of Turkish language processing. Moreover, there is a considerable shortage of human-annotated corpora derived from Turkish web data. The ITU Web Treebank is the first attempt for a diverse corpus compiled from Turkish texts found on the web. In this paper, we first present our survey of the non-canonical aspects of the language used in the Turkish web. Next, we discuss in detail the annotation procedure followed in the ITU Web Treebank, revised for compatibility with the language of the web. Finally, we describe the web-based annotation tool following this procedure, on which the treebank was annotated.

Text normalization is an indispensable stage in processing noncanonical language from natural sources, such as speech, social media or short text messages. Research in this field is very recent and mostly on English. As is known from different areas of natural language processing, morphologically rich languages (MRLs) pose many different challenges when compared to English. Turkish is a strong representative of MRLs and has particular normalization problems that may not be easily solved by a single-stage pure statistical model. This article introduces the first work on the social media text normalization of an MRL and presents the first complete social media text normalization system for Turkish. The article conducts an in-depth analysis of the error types encountered in Web 2.0 Turkish texts, categorizes them into seven groups and provides solutions for each of them by dividing the candidate generation task into separate modules working in a cascaded architecture. For the first time in the literature, two manually normalized Web 2.0 datasets are introduced for Turkish normalization studies. The exact match scores of the overall system on the provided datasets are 70.40 per cent and 67.37 per cent (77.07 per cent with a case insensitive evaluation).

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This article describes the procedures employed during the development of the first comprehensive machine-readable Turkish Sign Language (TiD) 1 resource: a bilingual lexical database and a parallel corpus between Turkish and TiD. In addition to sign language specific annotations (such as non-manual markers, classifiers and buoys) following the recently introduced TiD knowledge representation (Eryiğit et al. 2016), the parallel corpus contains also annotations of dependency relations, which makes it the first parallel treebank between a sign language and an auditory-vocal language.

Multiword expressions (MWEs) present particular and distinctive semantic properties, hence their automatic extraction receives special attention from the natural language processing (NLP) and corpus linguistics community, and is still an active research area. Unfortunately, the creation of necessary resources for this task is quite rigorous and many languages suffer from the lack of these; as in the case for Turkish.This study presents our MWE annotations on recently introduced Turkish Treebanks, which focuses on annotating various types of linguistic units and expressions, including named entities, numerical expressions, idiomatic phrases, verb phrases with auxiliaries and duplications. The paper aims to provide a benchmark and pave the way towards further MWE extraction research for Turkish. To this end, the paper also introduces our experimental results with seven baseline approaches, a dependency parser and a previously introduced rule-based extractor on these annotated corpora. Our highest performances achieved over these resources are about 60% F-scores.