3,336 research outputs found
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data
In recent years, the field of document understanding has progressed a lot. A
significant part of this progress has been possible thanks to the use of
language models pretrained on large amounts of documents. However, pretraining
corpora used in the domain of document understanding are single domain,
monolingual, or nonpublic. Our goal in this paper is to propose an efficient
pipeline for creating a big-scale, diverse, multilingual corpus of PDF files
from all over the Internet using Common Crawl, as PDF files are the most
canonical types of documents as considered in document understanding. We
analysed extensively all of the steps of the pipeline and proposed a solution
which is a trade-off between data quality and processing time. We also share a
CCpdf corpus in a form or an index of PDF files along with a script for
downloading them, which produces a collection useful for language model
pretraining. The dataset and tools published with this paper offer researchers
the opportunity to develop even better multilingual language models.Comment: Accepted at ICDAR 202
MC^2: A Multilingual Corpus of Minority Languages in China
Large-scale corpora play a vital role in the construction of large language
models (LLMs). However, existing LLMs exhibit limited abilities in
understanding low-resource languages, including the minority languages in
China, due to a lack of training data. To improve the accessibility of these
languages, we present MC^2, a Multilingual Corpus of Minority Languages in
China, which is the largest open-source corpus so far. It encompasses four
underrepresented languages, i.e., Tibetan, Uyghur, Kazakh in the Kazakh Arabic
script, and Mongolian in the traditional Mongolian script. Notably, two writing
systems in MC^2 are long neglected in previous corpora. As we identify serious
contamination in the low-resource language split in the existing multilingual
corpora, we propose a quality-centric solution for collecting MC^2,
prioritizing quality and accuracy while enhancing representativeness and
diversity. By in-depth analysis, we demonstrate the new research challenges
MC^2 brings, such as long-text modeling and multiplicity of writing systems. We
hope MC^2 can help enhance the equity of the underrepresented languages in
China and provide a reliable data foundation for further research on
low-resource languages.Comment: Work in progres
A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics
© {Owner/Author | ACM} {Year}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824876[EN] In this paper, we describe a hybrid approach for word-level language (WLL) identification of Bangla words written in Roman script and mixed with English words as part of our participation in the shared task on transliterated search at Forum for Information Retrieval Evaluation (FIRE) in 2014. A CRF based machine learning model and post-processing heuristics are employed for the WLL identification task. In addition to language identification, two transliteration systems were built to transliterate detected Bangla words written in Roman script into native Bangla script. The system demonstrated an overall token level language identification accuracy of 0.905. The token level Bangla and English language identification F-scores are 0.899, 0.920 respectively. The two transliteration systems achieved accuracies of 0.062 and 0.037. The word-level language identification system presented in this paper resulted in the best scores across almost all metrics among all the participating systems for the Bangla-English language pair.We acknowledge the support of the Department of Electronics and Information Technology (DeitY), Government of India, through the project “CLIA System Phase II”. The research work of the last author was carried out in the framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLICATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Banerjee, S.; Kuila, A.; Roy, A.; Naskar, SK.; Rosso, P.; Bandyopadhyay, S. (2014). A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics. 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