Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages
- URL: http://arxiv.org/abs/2404.09764v1
- Date: Mon, 15 Apr 2024 13:07:31 GMT
- Title: Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages
- Authors: Paramita Das, Isaac Johnson, Diego Saez-Trumper, Pablo Aragón,
- Abstract summary: We propose a novel computational framework for modeling the quality of Wikipedia articles.
Our framework is based on language-agnostic structural features extracted from the articles.
We have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia.
- Score: 0.19698344608599344
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use.
Related papers
- How Good is Your Wikipedia? [13.814955569390207]
This paper critically examines the data quality of Wikipedia in a non-English setting by subjecting it to various quality filtering techniques.
We find that data quality pruning is an effective means for resource-efficient training without hurting performance.
arXiv Detail & Related papers (2024-11-08T12:35:58Z) - An Open Multilingual System for Scoring Readability of Wikipedia [3.992677070507323]
We develop a multilingual model to score the readability of Wikipedia articles.
We create a novel multilingual dataset spanning 14 languages, by matching articles from Wikipedia to simplified Wikipedia and online childrens.
We show that our model performs well in a zero-shot scenario, yielding a ranking accuracy of more than 80% across 14 languages.
arXiv Detail & Related papers (2024-06-03T23:07:18Z) - WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in
Wikipedia [14.325320851640084]
We propose WikiSQE, the first large-scale dataset for sentence quality estimation in Wikipedia.
Each sentence is extracted from the entire revision history of English Wikipedia.
WikiSQE has about 3.4 M sentences with 153 quality labels.
arXiv Detail & Related papers (2023-05-10T06:45:13Z) - Mapping Process for the Task: Wikidata Statements to Text as Wikipedia
Sentences [68.8204255655161]
We propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level.
The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia.
We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models.
arXiv Detail & Related papers (2022-10-23T08:34:33Z) - WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions
from Paragraphs [66.88232442007062]
We introduce WikiDes, a dataset to generate short descriptions of Wikipedia articles.
The dataset consists of over 80k English samples on 6987 topics.
Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions.
arXiv Detail & Related papers (2022-09-27T01:28:02Z) - Whose Language Counts as High Quality? Measuring Language Ideologies in
Text Data Selection [83.3580786484122]
We find that newspapers from larger schools, located in wealthier, educated, and urban ZIP codes are more likely to be classified as high quality.
We argue that privileging any corpus as high quality entails a language ideology.
arXiv Detail & Related papers (2022-01-25T17:20:04Z) - Assessing the quality of sources in Wikidata across languages: a hybrid
approach [64.05097584373979]
We run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages.
We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata.
The findings help us ascertain the quality of references in Wikidata, and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web.
arXiv Detail & Related papers (2021-09-20T10:06:46Z) - Language-agnostic Topic Classification for Wikipedia [1.950869817974852]
We propose a language-agnostic approach based on the links in an article for classifying articles into a taxonomy of topics.
We show that it matches the performance of a language-dependent approach while being simpler and having much greater coverage.
arXiv Detail & Related papers (2021-02-26T22:17:50Z) - Generating Wikipedia Article Sections from Diverse Data Sources [57.23574577984244]
We benchmark several training and decoding strategies on WikiTableT.
Our qualitative analysis shows that the best approaches can generate fluent and high quality texts but they sometimes struggle with coherence.
arXiv Detail & Related papers (2020-12-29T19:35:34Z) - Multiple Texts as a Limiting Factor in Online Learning: Quantifying
(Dis-)similarities of Knowledge Networks across Languages [60.00219873112454]
We investigate the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted.
Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias.
The article builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.
arXiv Detail & Related papers (2020-08-05T11:11:55Z)
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.