CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation
- URL: http://arxiv.org/abs/2403.12721v2
- Date: Tue, 26 Mar 2024 14:32:34 GMT
- Title: CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation
- Authors: Nikola Ljubešić, Taja Kuzman,
- Abstract summary: This paper presents a collection of highly comparable web corpora of Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian.
The collection of these corpora comprises a total of 13 billion tokens of texts from 26 million documents.
All the corpora were linguistically annotated with the state-of-the-art CLASSLA-Stanza linguistic processing pipeline.
- Score: 4.450536872346658
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This paper presents a collection of highly comparable web corpora of Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian, covering thereby the whole spectrum of official languages in the South Slavic language space. The collection of these corpora comprises a total of 13 billion tokens of texts from 26 million documents. The comparability of the corpora is ensured by a comparable crawling setup and the usage of identical crawling and post-processing technology. All the corpora were linguistically annotated with the state-of-the-art CLASSLA-Stanza linguistic processing pipeline, and enriched with document-level genre information via the Transformer-based multilingual X-GENRE classifier, which further enhances comparability at the level of linguistic annotation and metadata enrichment. The genre-focused analysis of the resulting corpora shows a rather consistent distribution of genres throughout the seven corpora, with variations in the most prominent genre categories being well-explained by the economic strength of each language community. A comparison of the distribution of genre categories across the corpora indicates that web corpora from less developed countries primarily consist of news articles. Conversely, web corpora from economically more developed countries exhibit a smaller proportion of news content, with a greater presence of promotional and opinionated texts.
Related papers
- Entropy and type-token ratio in gigaword corpora [0.0]
We investigate entropy and text-token ratio, two metrics for lexical diversities, in six massive linguistic datasets in English, Spanish, and Turkish.
We find a functional relation between entropy and text-token ratio that holds across the corpora under consideration.
Our results contribute to the theoretical understanding of text structure and offer practical implications for fields like natural language processing.
arXiv Detail & Related papers (2024-11-15T14:40:59Z) - New Textual Corpora for Serbian Language Modeling [0.0]
The uniqueness of both old and new corpora will be accessed via frequency-based stylometric methods.
The paper will introduce three new corpora: a new umbrella web corpus of Serbo-Croatian, a new high-quality corpus based on the doctoral dissertations stored within National Repository of Doctoral dissertations from all Universities in Serbia, and a parallel corpus of abstract translation from the same source.
arXiv Detail & Related papers (2024-05-15T11:05:16Z) - MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling [70.34758460372629]
We introduce a new paradigm that encodes the same information with segments of consistent size across diverse languages.
MYTE produces shorter encodings for all 99 analyzed languages.
This, in turn, improves multilingual LM performance and diminishes the perplexity gap throughout diverse languages.
arXiv Detail & Related papers (2024-03-15T21:21:11Z) - Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora [1.0995326465245927]
We show that there are significant quality differences between different portions of web-mined corpora.
We also show that, for some web-mined datasets, Neural Machine Translation (NMT) models trained with their highest-ranked 25k portion can be on par with human-curated datasets.
arXiv Detail & Related papers (2024-02-12T07:03:14Z) - NusaWrites: Constructing High-Quality Corpora for Underrepresented and
Extremely Low-Resource Languages [54.808217147579036]
We conduct a case study on Indonesian local languages.
We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets.
Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
arXiv Detail & Related papers (2023-09-19T14:42:33Z) - T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text
Classification [50.675552118811]
Cross-lingual text classification is typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest.
We propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages.
arXiv Detail & Related papers (2023-06-08T07:33:22Z) - Corpus Similarity Measures Remain Robust Across Diverse Languages [0.0]
This paper experiments with frequency-based corpus similarity measures across 39 languages using a register prediction task.
The goal is to quantify (i) the distance between different corpora from the same language and (ii) the homogeneity of individual corpora.
Results show that measures of corpus similarity retain their validity across different language families, writing systems, and types of morphology.
arXiv Detail & Related papers (2022-06-09T08:17:16Z) - A New Generation of Perspective API: Efficient Multilingual
Character-level Transformers [66.9176610388952]
We present the fundamentals behind the next version of the Perspective API from Google Jigsaw.
At the heart of the approach is a single multilingual token-free Charformer model.
We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
arXiv Detail & Related papers (2022-02-22T20:55:31Z) - Are Multilingual Models the Best Choice for Moderately Under-resourced
Languages? A Comprehensive Assessment for Catalan [0.05277024349608833]
This work focuses on Catalan with the aim of exploring what extent a medium-sized monolingual language model is competitive with state-of-the-art large multilingual models.
We build a clean, high-quality textual Catalan corpus (CaText), train a Transformer-based language model for Catalan (BERTa), and devise a thorough evaluation in a diversity of settings.
The result is a new benchmark, the Catalan Language Understanding Benchmark (CLUB), which we publish as an open resource.
arXiv Detail & Related papers (2021-07-16T13:52:01Z) - Bridging Linguistic Typology and Multilingual Machine Translation with
Multi-View Language Representations [83.27475281544868]
We use singular vector canonical correlation analysis to study what kind of information is induced from each source.
We observe that our representations embed typology and strengthen correlations with language relationships.
We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
arXiv Detail & Related papers (2020-04-30T16:25:39Z) - Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual
Lexical Semantic Similarity [67.36239720463657]
Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages.
Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs.
Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
arXiv Detail & Related papers (2020-03-10T17:17:01Z)
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.