We present OTA-BOUN v2.0, the largest Universal Dependencies treebank for historical Turkish, consisting of 1,742 manually verified sentences sampled from late Ottoman texts. The annotation process followed a semi-automatic methodology: initial pre-annotation by the UDPipe 2.0 pipeline was refined through manual annotation of dependency relations, part-of-speech tags, and lemmas. A distinctive feature of OTA-BOUN is its dual-script representation: each sentence is provided both in the original Perso-Arabic script and its Latinized transcription, while tokens include aligned forms in both scripts. This dual-layer design enables research on script conversion, cross-lingual transfer, and historical–modern Turkish comparisons. Through detailed analyses on the aforementioned treebank, this study presents a unique and scalable resource, advancing computational studies of historical Turkish and supporting broader efforts in multilingual and diachronic NLP.

Named Entity Recognition (NER) in historical texts poses distinct challenges. Language change reflected in spelling variations, archaic vocabulary, and inconsistent orthography, diminish the efficacy of models trained on contemporary corpora. The limited availability of annotated historical datasets constrains the development and evaluation of accurate, domain-specific NER systems, underscoring the necessity for specialized approaches and domain adaptation. In this work, we introduce the ruznamçe registers as a valuable digital historical resource with broad potential for diverse NLP applications. Our primary contribution is RuznamceNER, a manually annotated NER dataset derived from ruznamçe documents spanning two centuries. The dataset contains 2,138 sentences and a total of 8,730 annotated entities of types PERSON, LOCATION and ORGANIZATION. We further report evaluation results using a BERT-CRF baseline model pre-trained with modern Turkish, highlighting the pivotal importance of in-domain training data for effective NER in historical contexts. Experimental results on the RuznamceNER test set under various training configurations show that even a small amount of supervised in-domain data can yield robust performance for well-structured texts, despite significant lexical and orthographic differences between historical and modern language forms

Antimicrobial resistance is a growing global health threat, driving interest in nanoparticle-based alternatives to conventional antibiotics. Inorganic nanoparticles (NPs) with intrinsic antibacterial properties show significant promise; however, efficiently identifying relevant studies from the rapidly expanding literature remains a major challenge. This step is crucial for enabling computational approaches that aim to model and predict NP efficacy based on physicochemical and structural features. In this study, we explore the effectiveness of traditional machine learning and deep learning methods in classifying scientific abstracts in the domain of NP-based antimicrobial research. We introduce the “Antibacterial Inorganic NAnoparticles Dataset” AINA of 7,910 articles, curated to distinguish intrinsic antibacterial NPs from studies focusing on drug carriers or surface-bound applications. Our comparative evaluation shows that a fine-tuned BioBERT classifier achieved the highest macro F1 (0.82), while a lightweight SVM model with TF-IDF features remained competitive (0.78), highlighting their utility in low-resource settings. AINA enables reproducible, large-scale identification of intrinsically bactericidal inorganic NPs. By reducing noise from non-intrinsic contexts, this work provides a foundation for mechanism-aware screening, database construction, and predictive modeling in antimicrobial NP research.

Arabic calligraphy carries rich historical information and meaning. However, the complexity of its artistic elements and the absence of a consistent baseline make text extraction from such works highly challenging. In this paper, we provide an in-depth analysis of the unique obstacles in processing and interpreting these images, including the variability in calligraphic styles, the influence of artistic distortions, and the challenges posed by missing or damaged text elements. We explore potential solutions by leveraging state-of-the-art architectures and deep learning models, including visual language models, to improve text extraction and script completion.

This paper introduces a novel, annotated Named Entity Recognition (NER) dataset derived from a collection of 181 news articles about the Nakba and its witnesses. Given their prominence as a primary source of information on the Nakba in Turkish, news articles were selected as the primary data source. Some 4,032 news sentences are collected from web sites of two news agencies, Anadolu Ajansı and TRTHaber. We applied a filtering process to make sure that only the news which contain witness testimonies regarding the ongoing Nakba are included in the dataset. After a semi-automatic annotation for entities of type Person, Location, and Organization, we obtained a NER dataset of 2,289 PERSON, 5,875 LOCATION, and 1,299 ORGANIZATION tags. We expect the dataset to be useful in several NLP tasks such as sentiment analysis and relation extraction for Nakba event while providing a new language resource for Turkish. As a future work, we aim to improve the dataset by increasing the number of news and entity types.

This study introduces a pretrained large language model-based annotation methodology of the first dependency treebank in Ottoman Turkish. Our experimental results show that, through iteratively i) pseudo-annotating data using a multilingual BERT-based parsing model, ii) manually correcting the pseudo-annotations, and iii) fine-tuning the parsing model with the corrected annotations, we speed up and simplify the challenging dependency annotation process. The resulting treebank, that will be a part of the Universal Dependencies (UD) project, will facilitate automated analysis of Ottoman Turkish documents, unlocking the linguistic richness embedded in this historical heritage.

Ottoman Turkish, as a historical variant of modern Turkish, suffers from a scarcity of available corpora and NLP models. This paper outlines our pioneering endeavors to address this gap by constructing a clean text corpus of Ottoman Turkish materials. We detail the challenges encountered in this process and offer potential solutions. Additionally, we present a case study wherein the created corpus is employed in continual pre-training of BERTurk, followed by evaluation of the model’s performance on the named entity recognition task for Ottoman Turkish. Preliminary experimental results suggest the effectiveness of our corpus in adapting existing models developed for modern Turkish to historical Turkish.

Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.

Morphological tagging of code-switching (CS) data becomes more challenging especially when language pairs composing the CS data have different morphological representations. In this paper, we explore a number of ways of implementing a language-aware morphological tagging method and present our approach for integrating language IDs into a transformer-based framework for CS morphological tagging. We perform our set of experiments on the Turkish-German SAGT Treebank. Experimental results show that including language IDs to the learning model significantly improves accuracy over other approaches.

This paper presents the first treebank for the Laz language, which is also the first Universal Dependencies Treebank for a South Caucasian language. This treebank aims to create a syntactically and morphologically annotated resource for further research. We also aim to document an endangered language in a systematic fashion within an inherently cross-linguistic framework: the Universal Dependencies Project (UD). As of now, our treebank consists of 576 sentences and 2,306 tokens annotated in light with the UD guidelines. We evaluated the treebank on the dependency parsing task using a pretrained multilingual parsing model, and the results are comparable with other low-resourced treebanks with no training set. We aim to expand our treebank in the near future to include 1,500 sentences. The bigger goal for our project is to create a set of treebanks for minority languages in Anatolia.

In this paper, we present the current version of two different treebanks, the re-annotation of the Turkish PUD Treebank and the first annotation of the Turkish National Corpus Universal Dependency (henceforth TNC-UD). The annotation of both treebanks, the Turkish PUD Treebank and TNC-UD, was carried out based on the decisions concerning linguistic adequacy of re-annotation of the Turkish IMST-UD Treebank (Türk et. al., forthcoming). Both of the treebanks were annotated with the same annotation process and morphological and syntactic analyses. The TNC-UD is planned to have 10,000 sentences. In this paper, we will present the first 500 sentences along with the annotation PUD Treebank. Moreover, this paper also offers the parsing results of a graph-based neural parser on the previous and re-annotated PUD, as well as the TNC-UD. In light of the comparisons, even though we observe a slight decrease in the attachment scores of the Turkish PUD treebank, we demonstrate that the annotation of the TNC-UD improves the parsing accuracy of Turkish. In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options. Both the treebanks, including a full edit-history and the annotation guidelines, and the custom software are publicly available under an open license online.

We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.

We introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization. We adapt and investigate the effects of two untyped dependency tree kernels, which have originally been proposed for relation extraction, to the multi-document summarization problem. In addition, we propose a series of novel dependency grammar based kernels to better represent the syntactic and semantic similarities among the sentences. The proposed methods incorporate the type information of the dependency relations for sentence similarity calculation. To our knowledge, this is the first study that investigates using dependency tree based sentence similarity for multi-document summarization.