The paper examines the grammatical behavior of the negative element used to negate predicates in non-verbal clauses in three Turkic languages: Azerbaijani, Kyrgyz, and Turkish. We focus on its interaction with verbal copulas, subject agreement, and the distribution of agreement suffixes, as well as its position within the predicate phrase. The study draws on both previously described corpus data and newly collected examples. Across all three languages, agreement features are realised on the negative element only in the absence of an overt copula. The agreement morphology involved is identical to that found with nominal, adjectival, and adverbial predicates. In all the languages examined, the negative element remains within the predicate phrase; thus, its position is syntactically constrained. At the same time, we observe differences among the languages in the degree to which the position of the negator is fixed within the predicate. In Turkish and Azerbaijani, regardless of which element of the nominal predicate it negates, the negator invariably follows the predicate. In Kyrgyz, by contrast, it consistently appears immediately after the element it negates within the predicate. These patterns suggest that the negative element behaves syntactically as a phrasal operator associated with non-verbal predicates. For annotation purposes, we therefore propose analysing the negative element as a negation modifier, assigning it the POS tag ADV and the dependency relation advmod:neg.

Identifying units, ’syntactic words’, for morphosyntactic analysis is important yet challenging for morphologically rich languages. In this paper we propose a set of guiding principles to determine units of morphosyntactic analysis, and apply them to the case of copular constructions in Turkic languages, in the context of Universal Dependencies (UD) framework. We also provide a survey of the practice in the Turkic UD treebanks published to date, and discuss the advantages and disadvantages of the proposed tokenisation for a selection of Turkic languages.

We introduce the first fully aligned and manually annotated parallel Universal Dependencies (UD) treebanks for four Turkic languages: Azerbaijani, Kyrgyz, Turkish, and Uzbek. These resources currently consist of 148 strategically selected sentences that illustrate typologically significant morphosyntactic phenomena across these related yet distinct languages. These parallel treebanks enable systematic comparative studies of Turkic syntax and may be instrumental in cross-lingual NLP applications. All treebanks are available as part of UD v2.16.

As part of our efforts to develop unified Universal Dependencies (UD) guidelines for Turkic languages, we evaluate multiple approaches to a difficult morphosyntactic phenomenon, pronominal locative expressions formed by a suffix -ki. These forms result in multiple syntactic words, with potentially conflicting morphological features, and participating in different dependency relations. We describe multiple approaches to the problem in current (and upcoming) Turkic UD treebanks, and show that none of them offers a solution that satisfies a number of constraints we consider (including constraints imposed by UD guidelines). This calls for a compromise with the ‘least damage’ that should be adopted by most, if not all, Turkic treebanks. Our discussion of the phenomenon and various annotation approaches may also help treebanking efforts for other languages or language families with similar constructions.

Pretrained language models and large language models are increasingly used to assist in a great variety of natural language tasks. In this work, we explore their use in evaluating the quality of alternative corpus annotation schemes. For this purpose, we analyze two alternative annotations of the Turkish BOUN treebank, versions 2.8 and 2.11, in the Universal Dependencies framework using large language models. Using a suitable prompt generated using treebank annotations, large language models are used to recover the surface forms of sentences. Based on the idea that the large language models capture the characteristics of the languages, we expect that the better annotation scheme would yield the sentences with higher success. The experiments conducted on a subset of the treebank show that the new annotation scheme (2.11) results in a successful recovery percentage of about 2 points higher. All the code developed for this work is available at https://github.com/boun-tabi/eval-ud .

The recent advances in natural language processing have predominantly favored well-resourced English-centric models, resulting in a significant gap with low-resource languages. In this work, we introduce TURNA, a language model developed for the low-resource language Turkish and is capable of both natural language understanding and generation tasks.TURNA is pretrained with an encoder-decoder architecture based on the unified framework UL2 with a diverse corpus that we specifically curated for this purpose. We evaluated TURNA with three generation tasks and five understanding tasks for Turkish. The results show that TURNA outperforms several multilingual models in both understanding and generation tasks and competes with monolingual Turkish models in understanding tasks.

Access to natural language processing resources is essential for their continuous improvement. This can be especially challenging in educational institutions where the software development effort required to package and release research outcomes may be overwhelming and under-recognized. Access towell-prepared and reliable research outcomes is important both for their developers as well as the greater research community. This paper presents an approach to address this concern with two main goals: (1) to create an open-source easily deployable platform where resources can be easily shared and explored, and (2) to use this platform to publish open-source Turkish NLP resources (datasets and tools) created by a research lab. The Turkish Natural Language Processing (TULAP) was designed and developed as an easy-to-use platform to share dataset and tool resources which supports interactive tool demos. Numerous open access Turkish NLP resources have been shared on TULAP. All tools are containerized to support portability for custom use. This paper describes the design, implementation, and deployment of TULAP with use cases (available at https://tulap.cmpe.boun.edu.tr/). A short video demonstrating our system is available at https://figshare.com/articles/media/TULAP_Demo/22179047.