This Indigenous Language Survived Russian Occupation. YouTube's search and recommendation algorithms are driving children to Russian-language content even when they seek out videos in Kyrgyz, creating a cultural shift that concerns some parents. When anthropology researcher Ashley McDermott was doing fieldwork in Kyrgyzstan a few years ago, she says many people voiced the same concern: Children were losing touch with their indigenous language. The Central Asian country of 7 million people was under Russian control for a century until 1991, but Kyrgyz (pronounced kur-giz) survived and remains widely spoken among adults. McDermott, a doctoral student at the University of Michigan, says she also heard that some kids in rural villages where Kyrgyz dominated had spontaneously learned to speak Russian.

Large language models (LLMs) have excelled in numerous benchmarks, advancing AI applications in both linguistic and non-linguistic tasks. However, this has primarily benefited well-resourced languages, leaving less-resourced ones (LRLs) at a disadvantage. In this paper, we highlight the current state of the NLP field in the specific LRL: kyrgyz tili. Human evaluation, including annotated datasets created by native speakers, remains an irreplaceable component of reliable NLP performance, especially for LRLs where automatic evaluations can fall short. In recent assessments of the resources for Turkic languages, Kyrgyz is labeled with the status 'Scraping By', a severely under-resourced language spoken by millions. This is concerning given the growing importance of the language, not only in Kyrgyzstan but also among diaspora communities where it holds no official status. We review prior efforts in the field, noting that many of the publicly available resources have only recently been developed, with few exceptions beyond dictionaries (the processed data used for the analysis is presented at https://kyrgyznlp.github.io/). While recent papers have made some headway, much more remains to be done. Despite interest and support from both business and government sectors in the Kyrgyz Republic, the situation for Kyrgyz language resources remains challenging. We stress the importance of community-driven efforts to build these resources, ensuring the future advancement sustainability. We then share our view of the most pressing challenges in Kyrgyz NLP. Finally, we propose a roadmap for future development in terms of research topics and language resources.

Like teenagers around the world, Maksat hasn't been to school in weeks. As Kyrgyzstan imposed quarantine restrictions, the 15-year-old feels isolated like never before. He has been trapped at home with a sister he doesn't get on with, a father he struggles to communicate with and a mother working abroad. He is comfortable talking only to an internet chat bot. Maksat (not his real name) feels alone and misunderstood.

Koustubh Sharma is what you could call a cat scientist with a daunting task, as a wildlife biologist studying one of the world's most magnificent, fluffy-tailed and elusive big cats: snow leopards. Based in Kyrgyzstan, Sharma spends a lot of time trying to solve the riddle of how to study the hard-to-study, threatened species. The alpine cats live in frigid, barren landscapes; roam hundreds of miles and are so adept at solitude that they're dubbed "ghosts of the mountain." In the nearly 11 years that Sharma has studied snow leopards in the highlands of Central Asia, he has seen the thick-furred, rosette-marked feline only twice. His one close encounter was with a large male with a scarred face in southern Mongolia, while standing on a mountain ledge near a freshly killed ibex, a favorite meal for snow leopards.

Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. A key problem in constructing these knowledge bases from sources like the web is overcoming the erroneous and incomplete information found in millions of candidate extractions. To solve this problem, we turn to semantics — using ontological constraints between candidate facts to eliminate errors. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.