Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation

Core Concepts

Creation of comparable web corpora for South Slavic languages enriched with linguistic and genre annotation.

Abstract

Introduces a collection of highly comparable web corpora for South Slavic languages.
Covers Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian.
Total of 13 billion tokens from 26 million documents.
Ensured comparability through crawling setup and technology usage.
Linguistically annotated using CLASSLA-Stanza pipeline.
Enriched with genre information via X-GENRE classifier.
Genre analysis shows consistent distribution across corpora.
News content varies based on economic strength of language community.

Stats

This paper presents a collection of highly comparable web corpora covering the whole spectrum of official languages in the South Slavic language space. The total corpus comprises 13 billion tokens from 26 million documents.

Quotes

"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."

Key Insights Distilled From

CLASSLA-web

by Niko... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12721.pdf

Deeper Inquiries

How can these web corpora be utilized to advance natural language processing for under-resourced languages?

The web corpora discussed in the context provide a valuable resource for advancing natural language processing (NLP) for under-resourced languages. These corpora, covering South Slavic languages, offer a substantial amount of text data that can be used to train and develop language models specific to these languages. By utilizing these corpora, researchers and developers can improve machine translation systems, text summarization algorithms, sentiment analysis tools, and other NLP applications tailored to the unique linguistic characteristics of each South Slavic language. Additionally, the linguistic annotation provided in these corpora enables more accurate analyses of morphosyntactic features and facilitates the development of robust NLP technologies.

What are the potential implications of relying on automated genre identification for linguistic analyses?

Automated genre identification plays a crucial role in linguistics by providing insights into the functional content of texts within a corpus. However, there are several potential implications associated with relying solely on automated genre identification for linguistic analyses:

Accuracy Concerns: Automated systems may not always accurately identify nuanced or complex genres due to variations in writing styles or ambiguous text content.
Bias Issues: The training data used to develop automated classifiers may introduce biases that impact genre categorization results.
Lack of Contextual Understanding: Automated systems may struggle with understanding contextual nuances that human analysts easily grasp when identifying genres.
Limited Flexibility: Automated systems might lack flexibility in adapting to new or emerging genres that do not fit predefined categories.

While automated genre identification offers efficiency and scalability benefits, it is essential to complement it with manual validation and human oversight to ensure accurate genre labeling in linguistic analyses.

How might economic factors influence the distribution of genres in web corpora?

Economic factors can significantly influence the distribution of genres within web corpora as observed from the analysis presented:

Promotion vs News Content: Countries with higher economic development levels tend to have a greater proportion of promotional content compared to news articles in their web corpus. This shift reflects how economically developed countries prioritize marketing and promotion over traditional news dissemination.
Variety Across Genres: More economically advanced nations exhibit diverse content types beyond news articles such as opinion pieces, legal documents, informational/explanatory texts due to increased digital presence across various sectors like business, law, education etc.
Correlation Analysis: Statistical correlations between GDP per capita levels and genre distributions reveal patterns where certain genres become more prevalent as countries progress economically while others decrease proportionally.

In essence,
economic prosperity influences both
the quantity
and diversity
of textual content available online,
shaping
the composition
of web corpuses based on different country contexts
and economic landscapes