N akba TR : A T urkish NER Dataset for Nakba Narratives (2025)

Abstract: 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.

## introduction the nakba, which is translated as "catastrophe" in arabic, is a term used for the mass displacement and dispossession of palestinians starting from the 1948 arab-israeli war. during many nakba events suffered by the palestinian people, hundreds of thousands were forced to flee their homes, property, and belongings. the expulsion of native people to the degree of ethnic cleansing has several catastrophic results, including a refugee crisis and generational trauma that continues to this day. today, there are again genocide and nakba events against the palestinian people, which have been escalating since the end of 2023. the narratives about the nakba events are important in many aspects, including cultural, legal, and historical significance. there are different types of sources where the narratives can be found. the news content from different outlets is one of the main sources of narratives in the turkish language. in this work, we generated a manually annotated named entity recognition (ner) dataset from websites of two news agencies. both annotations and collected text can be used for several nlp tasks such as relation extraction, sentiment analysis, and topic modeling related to the nakba event. furthermore, it will serve as a new language resource for turkish, a language often considered underrepresented in nlp research. ## related work early studies on named entity recognition (ner) began in the 1990s. since then, numerous researchers have explored various aspects of ner tasks @xcite . a ner dataset can be generated as a general purpose dataset or it can be a domain-specific dataset. among different sources of text corpora, news texts are one of the popular sources as they are easy to collect especially in digital format. person, location, and organization are the most common entity types in news ner datasets @xcite . most studies on ner in turkish texts have focused on modern texts, which can be categorized as either formal or informal (noisy). formal texts adhere to standard grammatical and orthographic rules, while informal texts may exhibit variations and deviations from these norms. in akkaya and can (2021), a transfer learning approach is used for ner in informal data with rarely-seen entity types. they add a crf layer that is trained on both formal text and a noisy text to a bilstm-crf model. they report 61.53% f1 score on noisy text. @xcite experiment with several datasets and architectures with a goal of providing a benchmarking platform for various nlp tasks. they achieve 93.8% f1 score on the wikiann dataset @xcite using bilstm-crf model and 93.07% f1 score with berturk-crf model. kılıç et al. (2020) report 82.31% f1 score on formal turkish texts which is obtained through a multilingual cased bert model. finally, @xcite present results for several models evaluated on some public datasets. they report, an f1 score of 96.10% achieved by electra-tr on a dataset of news articles and 92.26% f1 score obtained by convberturk on the wikiann dataset. ## dataset analysis we conducted some basic analyses regarding the annotated named entities on the nakbatr dataset. we plotted the mention frequencies of frequently occurring location names over time. figure 2 illustrates these plots for the trthaber and aa sections of the dataset, respectively. note that, we excluded the locations gazze (gaza), filistin (palestine), and i̇srail (israel) from this plot since the mention frequency of these location names are much more higher than any other location in the dataset. the figure is helpful in understanding the changing focus of the news. for instance, the coverage of the intensive assault of israeli army on northern gaza civilian areas like jabaliya and beit lahia is clearly traceable from both plots. although there are some common patterns between the two news sources, they have different coverage rates. this can be attributed to the difference in their focus, target audience, and perspective since aa provides a broader, more international one, while trthaber has more local perspective. we also conducted an analysis of the cooccurrence patterns of named entities within the dataset to explore relationships between entities. specifically, we counted the frequency of each named entity pair appearing in the same sentence throughout the dataset. to enhance the clarity of the visualization, we applied a filtering step to exclude entity pairs with a co-occurrence frequency lower than 10 for the trthaber section and a cooccurrence frequency lower than 18 for the aa section. the resulting co-occurrence graph provides a visual representation of frequently co-occurring entities, highlighting key connections within the data. figure 3 illustrates co-occurrence graphs, showing the relations between named entities in the aa and trthaber news in the nakbatr dataset. in the figure, different entity types are represented in different colors. although only the entity pairs that are most frequently observed in the dataset are included for clarity, this co-occurrence graph provides insights into the relationships among the entities in the narratives. for instance, the organization entity unrwa and the person entity lazzarini are located near in the trthaber graph which makes sense as philippe lazzarini is the commissionergeneral of the unrwa organization. other visible relationships are between the person entity oweis (saleem oweis, communications specialist at unicef middle east and north africa) and the organization entity bm (united nations) in the trthaber graph and districts in the north gaza located together in the aa graph. some real life entities can have multiple names as in the case with bm (un) and birleşmiş milletler (united nations). this situation is clearly visible in the aa graph in the figure. unifying multiple names for a single entity will therefore be beneficial before extracting relationships between entities. ## conclusion in this work, we introduced a novel, manually annotated, turkish ner dataset. the dataset comprises 3,957 news sentences collected from the websites of two prominent news agencies. 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 types person, location, and organization, we obtained a ner dataset of 2,289 person, 5,875 loca-tion, and 1,299 organization tags. the dataset can be extended to be useful in several nlp tasks such as relation extraction or sentiment analysis for the nakba event while providing a new language resource for turkish. as future work, we aim to improve the dataset by increasing the number of news and entity types.