Mongol rule in China stands as a remarkable example of the amalgamation of two distinct cultures—one sedentary and one agricultural. The progression of the Mongol conquest in both northern and southern China warrants special attention. Initially, the Mongol campaigns in northern China (1211–1234) were marked by excessive cruelty, city destruction, the conversion of lands into pastures, and the displacement of the conquered population. However, this strategy proved to be unproductive, yielding minimal benefits for the Mongols. The strategic proposal presented by Yelü Chucai 耶律楚材 (1189–1243), an adviser to Genghis Khan (Mong. Činggis qaγan, Temüǰin 1162–1227) and Ögedei Khan (Mong. Ögedei qaγan, 1186–1241), compelled the conquerors to reassess their subsequent plans. During the reign of the Mongols in China, the population was divided into four groups: the Mongols, the semu 色目, the northern Chinese, and the southern Chinese. The ethnic hierarchy during the Yuan Dynasty was a structured system that categorised the population into distinct classes, primarily to facilitate governance and maintain social order within the diverse and vast empire. This hierarchy had significant implications for the social, political, and economic life of the people under the Mongol rule. Moreover, the Mongols created their own centralised administrative system, which mostly excluded the Chinese from key government positions. The Chinese were often assigned to minor positions or given fewer opportunities for promotion. This study delves into the traits of the four-class system and the Mongol administrative system in China. The ethnic policy implemented by the Mongols against the conquered people during the Yuan dynasty had a significant impact on social relations, economic activity, and political stability in China, which partially contributed to the dynasty’s later downfall
The morpheme po55 in the Yanzhou (Jiande) dialect functions as either a verb of giving or an object marker (Cao 2017). In the former case, po55 patterns with the ditransitive verb gěi in Mandarin Chinese, where the mode of transfer is semantically underspecified, while in the latter case, po55 marks distinct thematic roles of the post-po55 NP, akin to bǎ. The multifunction of po55 results from the grammaticalization pathway from verb of giving/helping to object marker, as defined by Chappell (2007). Through the extensive comparison with bǎ and bǎ-sentences in this paper, we argue that the post-po55 NP must be affected in a specific way and is always associated with the resultative state, due to the realization of the event denoted by the VP. By employing the linking framework in Randall 2010, we propose a unified treatment of po55 based on causativity, which ultimately leads to the conclusion that causativity constitutes the crucial component of the underlying Conceptual Structure of po55, while the morpheme po55 is lexicalized as a strict causative item, albeit its dual status in the grammar.
Abstract Objective To systematically evaluate and compare the clinical effects of the socket shield technique (SST) and conventional immediate implant placement (CIIP) in the esthetic zone through meta-analysis. Methods A systematic search was conducted in PubMed, EMBASE, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Chinese Science and Technology Periodical Database (VIP), and Wanfang Database for studies comparing the clinical and aesthetic effects of SST and CIIP, with the retrieval period spanning from database inception to October 9, 2024. After independent literature screening, data extraction, and bias risk assessment were independently performed by two investigations according to inclusion and exclusion criteria. All data analyses were performed by RevMan 5.4 software. Results A total of 27 studies, including 22 randomized controlled trials and 5 non-randomized studies of interventions (NRSI), involving 1307 implants, were included in the meta-analysis. Meta-analysis demonstrated that SST significantly outperformed CIIP in reducing horizontal buccal bone loss (MD = −0.50, 95%CI [−0.60, −0.41], I2 = 97%) and vertical buccal bone loss (MD = −0.56, 95%CI [−0.64, −0.48], I = 78%), as well as improving the pink esthetic score (PES: MD = 1.25, 95%CI [0.93, 1.57], I = 90%) and implant stability quotient (ISQ: MD = 5.83, 95%CI [4.08, 7.57], I2 = 69%). No significant difference was observed in implant success rate (RR = 1.00, 95% CI [0.98, 1.02], I2 = 0%). Subgroup analyses (the height and thickness of buccal shield, bone grafting, and publication language) aligned with primary outcome (horizontal buccal bone loss), and sensitivity analysis confirmed stable results. Conclusion Based on the available evidence, SST demonstrated favorable outcomes in reducing buccal bone loss, enhancing esthetic outcomes and implant stability while maintaining comparable implant success rates to CIIP. Nevertheless, the technique exhibited technical sensitivity and a lack of standardized surgical protocols. Therefore, its clinical application should be approached with caution. Future high-quality studies with extended follow-up are required to validate long-term efficacy and establish standardized clinical guidelines.
Utilizing a panel dataset of 273 prefecture-level cities in China from 2000 to 2019, this study evaluates the impact of the low-carbon city pilot (LCCP) policy on green total factor productivity (GTFP). This study calculates GTFP via a hybrid function model of the epsilon-based measure. Using a staggered difference-in-differences framework, we found that the LCCP policy improves GTFP. Through its implementation, the LCCP policy exerts an increasingly positive impact. We identify promoting industrial structure optimization and technological innovation as two plausible underlying mechanisms. Heterogeneity analysis finds that the impact is more pronounced in central and western China, as well as in low-level administrative and low-marketization cities. This study provides empirical evidence for the optimization of the LCCP policy and the transformation of the low-carbon economy, and provides a basis for policy-making.
Classical Chinese, as the core carrier of Chinese culture, plays a crucial role in the inheritance and study of ancient literature. However, existing natural language processing models primarily optimize for Modern Chinese, resulting in inadequate performance on Classical Chinese. This paper presents a comprehensive solution for Classical Chinese language processing. By continuing pre-training and instruction fine-tuning on the LLaMA3-8B-Chinese model, we construct a large language model, WenyanGPT, which is specifically designed for Classical Chinese tasks. Additionally, we develop an evaluation benchmark dataset, WenyanBENCH. Experimental results on WenyanBENCH demonstrate that WenyanGPT significantly outperforms current advanced LLMs in various Classical Chinese tasks. We make the model's training data, instruction fine-tuning data\footnote, and evaluation benchmark dataset publicly available to promote further research and development in the field of Classical Chinese processing.
The release of top-performing open-weight LLMs has cemented China's role as a leading force in AI development. Do these models support languages spoken in China? Or do they speak the same languages as Western models? Comparing multilingual capabilities is important for two reasons. First, language ability provides insights into pre-training data curation, and thus into resource allocation and development priorities. Second, China has a long history of explicit language policy, varying between inclusivity of minority languages and a Mandarin-first policy. To test whether Chinese LLMs today reflect an agenda about China's languages, we test performance of Chinese and Western open-source LLMs on Asian regional and Chinese minority languages. Our experiments on Information Parity and reading comprehension show Chinese models' performance across these languages correlates strongly (r=0.93) with Western models', with the sole exception being better Mandarin. Sometimes, Chinese models cannot identify languages spoken by Chinese minorities such as Kazakh and Uyghur, even though they are good at French and German. These results provide a window into current development priorities, suggest options for future development, and indicate guidance for end users.
Recent incidents in certain online games and communities, where anonymity is guaranteed, show that unchecked inappropriate remarks frequently escalate into verbal abuse and even criminal behavior, raising significant social concerns. Consequently, there is a growing need for research on techniques that can detect inappropriate utterances within conversational texts to help build a safer communication environment. Although large-scale language models trained on Korean corpora and chain-of-thought reasoning have recently gained attention, research applying these approaches to inappropriate utterance detection remains limited. In this study, we propose a soft inductive bias approach that explicitly defines reasoning perspectives to guide the inference process, thereby promoting rational decision-making and preventing errors that may arise during reasoning. We fine-tune a Korean large language model using the proposed method and conduct both quantitative performance comparisons and qualitative evaluations across different training strategies. Experimental results show that the Kanana-1.5 model achieves an average accuracy of 87.0046, improving by approximately 3.89 percent over standard supervised learning. These findings indicate that the proposed method goes beyond simple knowledge imitation by large language models and enables more precise and consistent judgments through constrained reasoning perspectives, demonstrating its effectiveness for inappropriate utterance detection.
Metro Xinwen adalah program berita berbahasa Mandarin pertama di Indonesia yang ditayangkan di Metro TV. Acara ini menyuguhkan informasi yang berhubungan dengan masyarakat Tionghoa di Indonesia dan dunia seperti: bisnis, hiburan, pendidikan, sosial, budaya, internasional, politik, ekonomi, hukum, selebriti, kesehatan, dan olahraga. Acara ini pertama kali resmi mengudara pada tanggal 25 November 2000. Peneliti ingin mengetahui bagaimana tanggapan generasi muda Tionghoa Surabaya untuk mengamati minat mereka menonton Metro Xinwen. Penelitian ini menggunakan metode kualitatif, yaitu pengumpulan data yang dilakukan dengan wawancara secara langsung dan online. Subjek dari penelitian ini adalah 12 remaja Tionghoa Surabaya berusia antara 17 hingga 23 tahun yang memiliki kemampuan bahasa Mandarin. Hal tersebut dilatarbelakangi oleh pemahaman bahwa generasi muda Tionghoa di pulau Jawa memiliki minat yang rendah pada bahasa Mandarin. Hal tersebut juga mempengaruhi tontonan program berita berbahasa Mandarin yang rendah. Penulis ingin generasi muda Tionghoa Surabaya dapat memberikan preferensi, persepsi dan tanggapan terkait Metro Xinwen. Penelitian ini memberikan wawasan tentang apa yang membuat generasi muda Tionghoa Indonesia tertarik pada berita berbahasa Mandarin. Berdasarkan hasil penelitian dapat disimpulkan bahwa tanggapan generasi muda Tionghoa Surabaya untuk mengamati minat mereka menonton Metro Xinwen bersifat positif dan negatif. Selain itu didapat juga bahwa minat responden bervariasi, mulai dari menjadikan Metro Xinwen sebagai sumber pengetahuan tentang sejarah dan kesenian Tiongkok, mempelajari kosa kata baru bahasa Mandarin, hingga mengikuti berita ekonomi global dan geopolitik dalam bahasa Mandarin.
Persaingan bisnis yang semakin ketat membuat banyak perusahaan yang menjalin kerja sama dengan konsorsium atau aliansi strategis untuk mencapai tujuan bisnis yang lebih efektif dan efisien. Persepsi adalah proses yang memungkinkan kita mengorganisir informasi dan menafsirkan kesan terhadap lingkungan sekitar. Interaksi antara karyawan Indonesia PT X dan karyawan Konsorsium Tiongkok Y merupakan aspek penting dalam keberhasilan kerja sama ini. Penelitian ini menggunakan metode penelitian kualitatif dengan menggunakan pendekatan deskriptif dan induktif yang dilakukan pada PT X. Melalui metode penelitian ini, peneliti dapat memperoleh pemahaman mendalam mengenai persepsi karyawan Indonesia PT X terhadap Konsorsium Tiongkok Y. Data yang diperoleh berupa hasil wawancara yang telah penulis jadikan transkripsi dan akan dianalisis dengan coding untuk disesuaikan dengan kategori tematik. Hasil analisis menunjukkan bahwa persepsi positif terhadap kerja sama antara PT X dan Konsorsium Tiongkok Y memiliki dampak signifikan terhadap efisiensi operasional dan keberlanjutan perusahaan. Persepsi positif ini mendorong kerja sama yang lebih baik, meningkatkan produktivitas, dan menciptakan lingkungan kerja yang kondusif. Kemampuan inovasi teknologi Konsorsium Tiongkok Y juga mendapat pujian tinggi, karena telah memainkan peran penting dalam melindungi lingkungan.
Considering their important role in human life the study of emotions is of great interest for linguistic science. This article is dedicated to the analysis of the phenomenon of somatization of sadness and the explanation of differences in the levels of such somatization as reflected in language.
The analysis of literature has shown that expression of sadness with the help of somatic expressions is particularly prevalent in African, South-East Asian and Australian languages. Organs that are most often associated with emotions are the heart, liver and stomach and interoception plays a great role in creating an association between an organ and an emotion. With its help a person becomes aware of the physical changes taking place inside their body, which can be caused in particular by emotions. It was established that certain associations between organs and emotions come to exist due to “somatic bridges” while others form because of “semantic shift”. It was found that the frequency of the use of somatic expressions that express emotions was reduced in English during the industrialization and that similar changes are taking place today in Chinese.
In order to explain the differences in the level of somatization it is useful to turn to the triadic structure of concepts, in accordance to which the concept “sadness” has an experiential side (an interoceptive characterization of the emotion), a notional side (a definition, verbal representation etc.) and an evaluative side. It is hypothesized that an important role of somatic expression in the expression of sadness points to the importance of the experiential side while the use of abstract words is indicative of the notional side being important. The fact that the experiential side of a concept is considered to predate the notional side explains the direction of the diachronic change from stronger somatization of emotions towards their expression with the help of more abstract notions.
To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models. We show that worst-group performance does not depend on model size alone, but also on the compression method used. Additionally, we find that model compression does not always worsen the performance on minority subgroups. Altogether, our analysis serves to further research into the subgroup robustness of model compression.
Yanis Labrak, Adrien Bazoge, Beatrice Daille
et al.
Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began with Byte-Pair Encoding (BPE) and was later followed by the adoption of SentencePiece and WordPiece. While subword tokenization consistently outperforms character and word-level tokenization, the precise factors contributing to its success remain unclear. Key aspects such as the optimal segmentation granularity for diverse tasks and languages, the influence of data sources on tokenizers, and the role of morphological information in Indo-European languages remain insufficiently explored. This is particularly pertinent for biomedical terminology, characterized by specific rules governing morpheme combinations. Despite the agglutinative nature of biomedical terminology, existing language models do not explicitly incorporate this knowledge, leading to inconsistent tokenization strategies for common terms. In this paper, we seek to delve into the complexities of subword tokenization in French biomedical domain across a variety of NLP tasks and pinpoint areas where further enhancements can be made. We analyze classical tokenization algorithms, including BPE and SentencePiece, and introduce an original tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods.
Phillip Richter-Pechanski, Philipp Wiesenbach, Dominic M. Schwab
et al.
Automatic extraction of medical information from clinical documents poses several challenges: high costs of required clinical expertise, limited interpretability of model predictions, restricted computational resources and privacy regulations. Recent advances in domain-adaptation and prompting methods showed promising results with minimal training data using lightweight masked language models, which are suited for well-established interpretability methods. We are first to present a systematic evaluation of these methods in a low-resource setting, by performing multi-class section classification on German doctor's letters. We conduct extensive class-wise evaluations supported by Shapley values, to validate the quality of our small training data set and to ensure the interpretability of model predictions. We demonstrate that a lightweight, domain-adapted pretrained model, prompted with just 20 shots, outperforms a traditional classification model by 30.5% accuracy. Our results serve as a process-oriented guideline for clinical information extraction projects working with low-resource.
Abstract Based on the background of education informatics, this paper uses natural language processing technology to analyze the teaching effect in the Chinese language and literature. By analyzing the basic methods of processing natural language, the CBOW model is constructed using text vectorization. Combined with the LDA model for Chinese language literature text, a keyword is automatically extracted. GRU is chosen as the master control unit of the information feature loop. The CBOW model is optimized using the gradient descent optimization algorithm, which improves the analysis efficiency of education and teaching. Relevant strategies for improving humanistic quality are proposed by analyzing the humanistic quality of Chinese language and literature majors in colleges and universities using natural language processing technology. The results show that the CBOW model performs better in analyzing the textual features of Chinese language and literature, and its MR value is 0.8007±0.0028 compared with the traditional neural network models such as RNN, CNN, etc. Among the strategies for improving humanistic qualities under the education of Chinese language and literature, the effect of strengthening the dissemination of cultural knowledge is 0.8. This study promotes the education of Chinese language and literature in colleges and universities to a certain degree, which is It is conducive to improving students’ humanistic literacy.
To address the controversy on cognitive resources sharedness between language and music in semantic processing, two experiments were conducted via the interference paradigm using the Event-Related Potential (ERP) technique. In Experiment 1, a five-word sentence and a five-chord sequence were simultaneously presented in a trial. The sentence (e.g., '警察捡到了一部手机/钱包*,' The policeman found a mobile phone/wallet) ended with a semantically acceptable or unacceptable number-classifier-noun collocation (NCN), and the final chord of the chord sequence was congruent or incongruent with the preceding chords in tone. The stimuli in Experiment 1 were adapted in Experiment 2: The particle '了' was removed, and a three-word-long, object-gap relative clause was inserted ahead of the noun of the NCN in each sentence; two chords were inserted ahead of the third chord in each chord sequence. Both similarities and differences were revealed between Experiments 1 and 2, concerning the influences of the manipulated variables on the amplitude of the ERP component N400. In conclusion, the dissolution of semantic violation in sentence reading was likely to happen in parallel with music processing in chord sequence comprehension by non-musician Chinese native speakers, but interaction was observable between language and music in semantic processing when the sentences ended with long-distance NCNs.
This paper presents the first application of Native Language Identification (NLI) for the Turkish language. NLI is the task of automatically identifying an individual's native language (L1) based on their writing or speech in a non-native language (L2). While most NLI research has focused on L2 English, our study extends this scope to L2 Turkish by analyzing a corpus of texts written by native speakers of Albanian, Arabic and Persian. We leverage a cleaned version of the Turkish Learner Corpus and demonstrate the effectiveness of syntactic features, comparing a structural Part-of-Speech n-gram model to a hybrid model that retains function words. Our models achieve promising results, and we analyze the most predictive features to reveal L1-specific transfer effects. We make our data and code publicly available for further study.
The process of conducting literature reviews is often time-consuming and labor-intensive. To streamline this process, I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review. This tool leverages the power of open access science, large language models (LLMs) and natural language processing to enable the searching, downloading, and organizing of PDF files, as well as extracting content from articles. Semantic search queries are used for data retrieval, while text embeddings and summarization using LLMs present succinct literature reviews. Interaction with PDFs is enhanced through a user-friendly graphical user interface (GUI). The suite also features integrated programs for bibliographic organization, interaction and query, and literature review summaries. This tool presents a robust solution to automate and optimize the process of literature review in academic and industrial research.
Anastasia Kritharoula, Maria Lymperaiou, Giorgos Stamou
Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set of approaches. Since VWSD is primarily a text-image retrieval task, we explore the latest transformer-based methods for multimodal retrieval. Additionally, we utilize Large Language Models (LLMs) as knowledge bases to enhance the given phrases and resolve ambiguity related to the target word. We also study VWSD as a unimodal problem by converting to text-to-text and image-to-image retrieval, as well as question-answering (QA), to fully explore the capabilities of relevant models. To tap into the implicit knowledge of LLMs, we experiment with Chain-of-Thought (CoT) prompting to guide explainable answer generation. On top of all, we train a learn to rank (LTR) model in order to combine our different modules, achieving competitive ranking results. Extensive experiments on VWSD demonstrate valuable insights to effectively drive future directions.
Abstract This paper constructs a Bayesian network text recognition model based on the Bayesian network and explores the role of Chinese language literature in the dissemination of traditional culture by analyzing the embodiment of traditional culture in Chinese language literature network texts. The collection process of Chinese language and literature data in network text is analyzed from the perspective of textual data interaction. The information of node variables in a Bayesian network is used to determine the mutual relationship between Chinese language literature and traditional culture. The degree of interdependence between Chinese literature and traditional culture can be measured by combining mutual information. The results show that the correct rate of text recognition of the Bayesian text recognition model decreases slightly when the training samples are (100-300), but the correct rate always stays around 0.85, thus reflecting the effectiveness of the network recognition model in this paper. Chinese language literature has a certain role in the dissemination of traditional culture, which proves that Chinese language literature, as a carrier of traditional culture, can improve the dissemination speed of traditional culture. This study focuses on the integration of Chinese literature and traditional communication to improve a new vision.