Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing

• URL: http://arxiv.org/abs/2410.08918v1
• Date: Fri, 11 Oct 2024 15:46:09 GMT
• Title: Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing
• Authors: Isaac Johnson, Lucie-Aimée Kaffee, Miriam Redi,
• Abstract summary: We review the different ways in which Wikimedia data is curated to use in NLP tasks across pre-training, post-training, and model evaluations. We call for incorporating additional sources of Wikimedia data, a greater focus on benchmarks for LLMs that encode Wikimedia principles, and greater multilingualism in Wikimedia-derived datasets.
• Score: 3.960519546131734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wikimedia content is used extensively by the AI community and within the language modeling community in particular. In this paper, we provide a review of the different ways in which Wikimedia data is curated to use in NLP tasks across pre-training, post-training, and model evaluations. We point to opportunities for greater use of Wikimedia content but also identify ways in which the language modeling community could better center the needs of Wikimedia editors. In particular, we call for incorporating additional sources of Wikimedia data, a greater focus on benchmarks for LLMs that encode Wikimedia principles, and greater multilingualism in Wikimedia-derived datasets.

Related papers

• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia [10.21762162291523]
  We propose the novel task of captioning Wikipedia images by integrating contextual knowledge. Specifically, we produce models that jointly reason over Wikipedia articles, Wikimedia images and their associated descriptions.
  arXiv  Detail & Related papers  (2022-09-21T16:14:15Z)
• Considerations for Multilingual Wikipedia Research [1.5736899098702972]
  Non-English language editions of Wikipedia have led to the inclusion of many more language editions in datasets and models. This paper seeks to provide some background to help researchers think about what differences might arise between different language editions of Wikipedia.
  arXiv  Detail & Related papers  (2022-04-05T20:34:15Z)
• A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models [0.0]
  We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks. We introduce a new corpus of Icelandic text, the Icelandic Common Crawl Corpus (IC3), a collection of high quality texts found online by targeting the Icelandic top-level-domain (TLD) We show that a properly cleaned crawled corpus is sufficient to achieve state-of-the-art results in NLP applications for low to medium resource languages.
  arXiv  Detail & Related papers  (2022-01-14T18:45:31Z)
• EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching [90.98122161162644]
  Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. We propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning. We exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore.
  arXiv  Detail & Related papers  (2021-11-17T06:02:43Z)
• 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)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language [148.0843278195794]
  We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning. Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
  arXiv  Detail & Related papers  (2020-11-18T20:21:19Z)
• Named Entity Recognition for Social Media Texts with Semantic Augmentation [70.44281443975554]
  Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts. We propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
  arXiv  Detail & Related papers  (2020-10-29T10:06:46Z)
• Crosslingual Topic Modeling with WikiPDA [15.198979978589476]
  We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA) It learns to represent Wikipedia articles written in any language as distributions over a common set of language-independent topics. We show its utility in two applications: a study of topical biases in 28 Wikipedia editions, and crosslingual supervised classification.
  arXiv  Detail & Related papers  (2020-09-23T15:19:27Z)

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.