arXiv:2608. 00523v2 Announce Type: replace-cross Abstract: The linguistic notion of state has traditionally been restricted to the construct (annexation) state of Afroasiatic languages and treated as a language-specific morphosyntactic phenomenon.
The article examines how Byte‑Pair Encoding (BPE) tokenization handles Polish, an inflectional language, and finds that BPE tends to stabilize frequent surface fragments of grammatical exponents rather than true grammatical categories. It introduces the concept of grammatical form anchoring, showing that certain Polish verb forms can signal the speaking subject without an explicit pronoun, and highlights that language models may lack a stable grammatical "I" and can shift gender or mirror user forms. The study proposes Roclawski’s segmentation‑flexional forms as a diagnostic framework and suggests that more stable Polish modeling would require sublexical stabilization, anchoring grammatical form in the inflectional system, and maintaining the grammatical "I" in dialogue.
arXiv:2608. 01935v1 Announce Type: cross Abstract: Prior work in Ancient Greek NLP relies on corpora that do not disambiguate the phonemic vowel length of alpha, iota, and ypsilon, together known as the dichrona.
This article tackles an important phenomenon in the syntax of Yemeni Ibbi Arabic (YIA), viz. , wh-agreement, a phenomenon common to several languages including Greek, Indonesian, Lubukusu, Irish, etc.
arXiv:2609.39572v1 Announce Type: new Abstract: We analyze how lexical ambiguity and underspecification affect language model training. We create artificial homonyms and artificial hypernyms as pseud...
The study investigates whether pretrained transformer models encode functional words—such as pronouns and adverbs—in a way that mirrors human usage. By comparing embeddings of nouns with those of their functional counterparts in both isolated and parallel sentences, the authors find that functional words occupy a central yet distinct position in embedding space and that parallel lexicalized and functional sentences reside in different subspaces. Experiments show that only a mixed training set of functional and lexicalized sentences reveals shared syntactic and semantic structure, whereas training on either type alone fails to capture this parallelism.
The paper introduces the Fixed Suffix Dependency Ratio (FSDR) as a metric to measure how much loanwords depend on fixed derivational suffixes for gender assignment. Analyzing 1,832 Latvian noun lemmas, it finds that feminine loanwords rely more on fixed suffixes while masculine loanwords are largely free‑choice, a pattern that has intensified in recent usage. FSDR offers a quantitative tool for distinguishing morphological anchoring from default gender in language contact scenarios.
The paper introduces the Fixed Suffix Dependency Ratio (FSDR) as a metric to measure how much loanwords depend on fixed derivational suffixes for gender assignment. Analyzing 1,832 Latvian noun lemmas, it finds that feminine loanwords rely more on fixed suffixes, whereas masculine loanwords are more often assigned freely, a pattern that has intensified in recent usage. This asymmetry highlights a dual-track mechanism in gender assignment under language contact.
arXiv:2606. 15144v1 Announce Type: cross Abstract: Large language models (LLMs) process text as sequences of subword tokens, which can obscure the character-level and morphological structure that underlies word formation.
The paper investigates how different representations of Korean constituency structure affect parsing performance. It compares three formats—Morpheme+XPOS, Eojeol+XPOS, and Eojeol+UPOS—derived from the Penn Korean Treebank, using gold segmentation and labels to evaluate transition-based parsers. Results show that fine-grained morphological and XPOS information yields the best parsing accuracy, while eojeol-based representations offer shorter transition sequences but lower performance when only UPOS is used.
arXiv:2608.28924v1 Announce Type: new Abstract: Linguistic theory has long recognized cross-linguistic syntactic regularities, leading to claims that these similar structures are processed by similar...
The paper introduces MoirfEolas, a dataset of over 35,000 Irish words annotated with their morphological components, and CríochScore, a metric that measures how well tokenization aligns with these morphological boundaries. Using CríochScore, the authors evaluate common tokenization algorithms and find that the Unigram Language Model best aligns with Irish morphology. They also discuss trade‑offs between morphological alignment, compression, and vocabulary efficiency, offering practical guidance for Irish NLP development.