MorfFlex: Handling Rich Morphology
- URL: http://arxiv.org/abs/2606.24366v1
- Date: Tue, 23 Jun 2026 09:56:58 GMT
- Title: MorfFlex: Handling Rich Morphology
- Abstract summary: We present MorfFlex, a morphological dictionary architecture suitable for languages with extensive regularity in both inflection and derivation.<n>As the primary example of MorfFlex in use we introduce MorfFlex CZ, a morphological dictionary of Czech.<n>It is distributed as a simple, unstructured list of wordform, lemma, tag> triplets, however, its manually maintained, unpublished source files and conversion scripts encode a sophisticated system of inflectional and derivational patterns.
- Score: 1.4766181043669164
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present MorfFlex, a morphological dictionary architecture suitable for languages with extensive regularity in both inflection and derivation. As the primary example of MorfFlex in use we introduce MorfFlex CZ, a morphological dictionary of Czech. It is distributed as a simple, unstructured list of <wordform, lemma, tag> triplets, however, its manually maintained, unpublished source files and conversion scripts encode a sophisticated system of inflectional and derivational patterns. These patterns dramatically reduce the otherwise enormous size of the dictionary, which currently contains over 100 million wordforms and more than 1 million lemmas. The MorfFlex CZ dictionary serves as an essential resource for ensuring the consistency of manual morphological annotation in the Prague Dependency Treebanks and underpins state-of-the-art automatic tools such as MorphoDiTa. In this paper, we focus on: (i) presenting an effective method for managing the rich morphological system within the dictionary, and (ii) demonstrating the utility of such a language resource for maintaining annotation consistency in corpora and supporting the development of advanced NLP applications.
Related papers
- Weave of Formal Thought [51.56484100374058]
We introduce Weave of Formal Thought (WoFT), a paradigm uniting rigorous syntactic validation with learned structural representations.<n>We present a latent-variable fine-tuning method training the language model to interleave non-terminal grammar symbols directly into generation.<n>For Python, fine-tuning StarCoder2-3B with our RWS objective reduces per-token cross-entropy by 14.3% relative to a text-only SFT baseline.
arXiv Detail & Related papers (2026-06-24T15:58:11Z) - LGSE: Lexically Grounded Subword Embedding Initialization for Low-Resource Language Adaptation [7.623227616015147]
We propose the Lexically Grounded Subword Embedding Initialization framework, which introduces morphologically informed segmentation for initializing embeddings of novel tokens.<n>Instead of using random vectors or arbitrary subwords, LGSE decomposes words into their constituent morphemes and constructs semantically coherent embeddings.<n>We evaluate LGSE on three NLP tasks: Question Answering, Named Entity Recognition, and Text Classification, in two morphologically rich, low-resource languages.
arXiv Detail & Related papers (2026-03-23T23:07:16Z) - MoVoC: Morphology-Aware Subword Construction for Geez Script Languages [7.7761618950496265]
Subword-based tokenization methods often fail to preserve morphological boundaries.<n>We present MoVoC (Morpheme-aware Subword Vocabulary Construction) and train MoVoC-Tok, a tokenizer that integrates supervised morphological analysis into the subword vocabulary.
arXiv Detail & Related papers (2025-09-10T17:45:10Z) - Unsupervised Morphological Tree Tokenizer [36.584680344291556]
We introduce morphological structure guidance to tokenization and propose a deep model to induce character-level structures of words.<n>Our method is capable of inducing character-level structures that align with morphological rules without annotated training data.<n> Empirical results indicate that the proposed method effectively retains complete morphemes and outperforms widely adopted methods such as BPE and WordPiece.
arXiv Detail & Related papers (2024-06-21T15:35:49Z) - A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching [60.51839859852572]
We propose to resolve the text into multi concepts for multilingual semantic matching to liberate the model from the reliance on NER models.
We conduct comprehensive experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
arXiv Detail & Related papers (2024-03-05T13:55:16Z) - UniMorph 4.0: Universal Morphology [104.69846084893298]
This paper presents the expansions and improvements made on several fronts over the last couple of years.
Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages.
In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages.
arXiv Detail & Related papers (2022-05-07T09:19:02Z) - CKMorph: A Comprehensive Morphological Analyzer for Central Kurdish [4.226093500082746]
A morphological analyzer is a significant component of many natural language processing applications especially for morphologically rich languages.
This paper introduces a comprehensive morphological analyzer for Central Kurdish (CK), a low-resourced language with a rich morphology.
CKMorph was able to correctly analyze 95.9% of the accuracy test set, containing 1,000 CK words morphologically analyzed according to the context.
arXiv Detail & Related papers (2021-09-17T15:45:27Z) - Evaluating the Morphosyntactic Well-formedness of Generated Texts [88.20502652494521]
We propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text.
We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.
arXiv Detail & Related papers (2021-03-30T18:02:58Z) - Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size.
We propose a fully compositional output embedding layer for language models.
To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
arXiv Detail & Related papers (2020-09-24T07:21:14Z) - A Simple Joint Model for Improved Contextual Neural Lemmatization [60.802451210656805]
We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages.
Our paper describes the model in addition to training and decoding procedures.
arXiv Detail & Related papers (2019-04-04T02:03:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.