Aug 2026· Linguistics Vanguard· 0 citations· 62 references
TL;DR
This paper uses morphological feature annotations from the Universal Dependencies corpora to quantify information carried by morphology across 154 different datasets spanning 72 language varieties, and proposes an information-theoretic approach that measures how surprising morphological feature values are given a token’s part of speech or lemma in the corpus.
Abstract
Abstract The emergence of large, consistently annotated corpora in many languages opens new avenues for linguistic typology by enabling the incorporation of usage-based evidence, including frequency information, into the study of language complexity. Morphological information is one of several facets of language complexity. In this paper, we use morphological feature annotations from the Universal Dependencies (UD) corpora to quantify information carried by morphology across 154 different datasets spanning 72 language varieties. We propose an information-theoretic approach that measures how surprising morphological feature values are given a token’s part of speech or lemma in the corpus. These token-level quantities are aggregated to dataset-level averages, yielding a usage-weighted estimate of morphological information load. We find substantial cross-linguistic variation in morphological information, and observe moderate to strong correlations with a related information-theoretic metric proposed by Çöltekin and Rama. By contrast, we find little correspondence with questionnaire-based typological metrics derived from Grambank, which represents an alternative approach to cross-linguistic comparison based on grammatical inventories rather than usage. This illustrates the difference between studying the possible extent of the grammatical system versus language use. We also discuss various drawbacks with corpus-based typology, such as comparability of datasets and uneven coverage across the globe.
We present CzeLeD, a large-scale lexical decision dataset for Czech containing 12,242 lemmas and inflected word forms derived from the HeCz self-paced reading corpus and supplemented with pseudowords. A total of 1,977 participants contributed over 849,000 trials, accompanied by demographic data and extensive linguistic annotation, including lemma and word-form frequencies, neighborhood density, phonotactic and orthotactic probability. Alongside the trial-level dataset, we provide lexical decision norms summarizing reaction times and accuracy using a transparent and reproducible trimming procedure. Data validation drew on both response accuracy and reaction times. Participants showed high overall accuracy (94.79%), and reaction times exhibited the expected strong inverse relationship with word form frequency. Importantly, CzeLeD can be directly linked to the HeCz corpus, enabling systematic comparison between word recognition in isolation and in sentential context. By combining broad lexical coverage, rich annotation, and seamless integration with an existing context-bound processing corpus, CzeLeD offers a powerful, reusable resource for investigating lexical processing, morphological complexity, and contextual effects in Czech and beyond.
J. Chromý, Markéta Ceháková, Mikuláš Preininger et al.· Scientific Data· 0 citations
This study examines the four major word classes (viz. noun, verb, adjective and adverb) in Lohorung, a Kirat Rai (Tibeto-Burman) language of north-eastern Nepal, based on their semantic, syntactic, and morphological properties, based on Givón’s formal and functional approach. Data were collected through elicitation with native speakers and textual analyses and were analysed with reference to the author’s native-speaker intuition. The findings indicate that Lohorung exhibits prototypical nouns with their multiple features and non-prototypical nouns as well. Morphologically, nouns are marked by the specific affixes, <-ʈsi> encodes dual and <-i> encodes plural. Lohorung employs three types of classifiers, namely <-tsi> <-kɔ>, and <-pɑŋ/pɑ>. Verbs are clause-final and serve as main predicates, typical of Tibeto-Burman languages. Lohorung has three numbers and persons systems with clusivity. Manner adverbials may take , and some adverbs modify adjectives. This study concludes that the analysis of the four major word classes enhances the grammatical description of the Lohorung language and contributes to functional-typological research on lexical classification.
Diwas Rai· Journal of Indigenous Knowle...· 0 citations
The efficiency metrics showed the tool to be effective in accurately identifying the target suffixes across proficiency levels, with a significant reduction in time and effort.
Ana Abigahil Flores-Hernández, Roilhi Frajo Ibarra-Hernández, P. Moore et al.· International Journal of Lea...· 0 citations
Automatic morphological analysis remains a challenging task for agglutinative languages because of their rich inflectional systems, productive derivation, and complex morphophonological rules. Recent neural models, especially transformer-based architectures, have demonstrated impressive empirical performance. However, they frequently exhibit a deficiency in linguistic transparency and encounter challenges in systematic generalization inside low-resource environments. This research offers a comparative and integrative examination of formal (rule-based and finite-state) and neural (KazBERT-based) methodologies for morphological analysis, utilizing the Kazakh language as a case study of low-resource agglutinative morphology. Initially present a formal morphological model that distinctly represents root-affix structure, vowel harmony, and morphotactic restrictions. We next test many neural architectures for morphological disambiguation and tagging, such as KazBERT coupled with CRF-based decoding. In addition to typical accuracy measurements, we do a comprehensive error taxonomy and linguistic analysis, investigating how various model classes manage ambiguity, infrequent forms, and extended affix chains. The findings indicate that whereas neural models excel in surface-level accuracy compared to exclusively rule-based systems, they demonstrate consistent deficiencies in morphologically intricate and infrequent constructs. On the other hand, formal models show better generalization based on language limitations. Based on these results, we suggest a hybrid morphology-aware framework that adds symbolic restrictions to neural inference. This framework consistently improves results in a variety of assessment contexts. The study demonstrates that effective morphological analysis of agglutinative languages requires the integration of neural representation learning with explicit linguistic structure. The results are not tied to any one language and have wider implications for morphology-sensitive NLP in low-resource settings.
A. Aitim, Ә.Қ. Әйтім, Халықаралық Ақпараттық et al.· Bulletin of Shakarim Univers...· 0 citations
The experimental results revealed the strengths and weaknesses of various approaches to subword segmentation and identified the most effective tokenization strategies under the conditions of the morphological complexity of the Tajik language.
M. Arabov, S. Khaybullina· Automatic Documentation and...· 0 citations
Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
D. Akhmedjanova· Международный Журнал Теорети...· 0 citations
