Slovak Conceptual Dictionary
- URL: http://arxiv.org/abs/2512.00579v1
- Date: Sat, 29 Nov 2025 18:15:28 GMT
- Title: Slovak Conceptual Dictionary
- Authors: Miroslav Blšták,
- Abstract summary: We introduce a conceptual dictionary for the Slovak language as the first linguistic tool of this kind.<n>Since Slovak language is a language with limited linguistic resources, there are currently not available any machine-readable linguistic data sources with a sufficiently large volume of data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: When solving tasks in the field of natural language processing, we sometimes need dictionary tools, such as lexicons, word form dictionaries or knowledge bases. However, the availability of dictionary data is insufficient in many languages, especially in the case of low resourced languages. In this article, we introduce a new conceptual dictionary for the Slovak language as the first linguistic tool of this kind. Since Slovak language is a language with limited linguistic resources and there are currently not available any machine-readable linguistic data sources with a sufficiently large volume of data, many tasks which require automated processing of Slovak text achieve weaker results compared to other languages and are almost impossible to solve.
Related papers
- Dictionaries to the Rescue: Cross-Lingual Vocabulary Transfer for Low-Resource Languages Using Bilingual Dictionaries [22.562544826766917]
Cross-lingual vocabulary transfer plays a promising role in adapting pre-trained language models to new languages.<n>Existing approaches that utilize monolingual or parallel corpora face challenges when applied to languages with limited resources.
arXiv Detail & Related papers (2025-06-02T10:52:52Z) - Zero-shot Sentiment Analysis in Low-Resource Languages Using a
Multilingual Sentiment Lexicon [78.12363425794214]
We focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets.
We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets.
arXiv Detail & Related papers (2024-02-03T10:41:05Z) - Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally.
Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
arXiv Detail & Related papers (2023-06-13T08:08:08Z) - LIMIT: Language Identification, Misidentification, and Translation using
Hierarchical Models in 350+ Languages [27.675441924635294]
Current systems cannot accurately identify most of the world's 7000 languages.
We first compile a corpus, MCS-350, of 50K multilingual and parallel children's stories in 350+ languages.
We propose a novel misprediction-resolution hierarchical model, LIMIt, for language identification.
arXiv Detail & Related papers (2023-05-23T17:15:43Z) - Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource
Languages [1.8787713898828164]
We present a detailed analysis of the effects of the quality of dictionaries, training dataset size, language family, etc., on the translation quality.
Results on multiple low-resource test languages show a clear advantage of our bilingual dictionary-based method over the baselines.
arXiv Detail & Related papers (2022-06-09T12:03:29Z) - Allocating Large Vocabulary Capacity for Cross-lingual Language Model
Pre-training [59.571632468137075]
We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity.
We propose an algorithm VoCap to determine the desired vocabulary capacity of each language.
In order to address the issues, we propose k-NN-based target sampling to accelerate the expensive softmax.
arXiv Detail & Related papers (2021-09-15T14:04:16Z) - Cross-lingual Transfer for Text Classification with Dictionary-based
Heterogeneous Graph [10.64488240379972]
In cross-lingual text classification, it is required that task-specific training data in high-resource source languages are available.
Collecting such training data can be infeasible because of the labeling cost, task characteristics, and privacy concerns.
This paper proposes an alternative solution that uses only task-independent word embeddings of high-resource languages and bilingual dictionaries.
arXiv Detail & Related papers (2021-09-09T16:40:40Z) - When Word Embeddings Become Endangered [0.685316573653194]
We present a method for constructing word embeddings for endangered languages using existing word embeddings of different resource-rich languages and translation dictionaries of resource-poor languages.
All our cross-lingual word embeddings and the sentiment analysis model have been released openly via an easy-to-use Python library.
arXiv Detail & Related papers (2021-03-24T15:42:53Z) - UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks.
Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages.
We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
arXiv Detail & Related papers (2020-12-31T11:37:28Z) - Data Augmentation and Terminology Integration for Domain-Specific
Sinhala-English-Tamil Statistical Machine Translation [1.1470070927586016]
Out of vocabulary (OOV) is a problem in the context of Machine Translation (MT) in low-resourced languages.
This paper focuses on data augmentation techniques where bilingual lexicon terms are expanded based on case-markers.
arXiv Detail & Related papers (2020-11-05T13:58:32Z) - Learning Contextualised Cross-lingual Word Embeddings and Alignments for
Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus.
Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
arXiv Detail & Related papers (2020-10-27T22:24:01Z) - Learning to Scale Multilingual Representations for Vision-Language Tasks [51.27839182889422]
The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date.
We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.
arXiv Detail & Related papers (2020-04-09T01:03:44Z)
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.