Refine
Document Type
- Conference Proceeding (2)
- Article (1)
- Part of a Book (1)
Language
- English (4)
Has Fulltext
- yes (4)
Keywords
- Korpus <Linguistik> (3)
- Automatische Sprachanalyse (2)
- corpus linguistics (2)
- Computerlinguistik (1)
- Lemma (1)
- Natürliche Sprache (1)
- Open Source (1)
- Semantik (1)
- Standard (1)
- Standardisierung (1)
Publicationstate
Reviewstate
- Peer-Review (2)
Our paper outlines a proposal for the consistent modeling of heterogeneous lexical structures in semasiological dictionaries, based on the element structures described in detail in chapter 9 (Dictionaries) of the TEI Guidelines. The core of our proposal describes a system of relatively autonomous lexical “crystals” that can, within the constraints of the relevant element’s definition, be combined to form complex structures for the description of morphological form, grammatical information, etymology, word-formation, and meaning for a lexical structure.
The encoding structures we suggest guarantee sustainability and support re-usability and interoperability of data. This paper presents case studies of encoding dictionary entries in order to illustrate our concepts and test their usability.
We comment on encoding issues involving <entry>, <form>, <etym>, and on refinements to the internal content of <sense>.
Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.
Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus
(2021)
Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.