A curated collection of linguistic resources, datasets, and tools for Natural Language Processing and Computational Linguistics on Spanish.
Awesome Spanish NLP is a curated, community-maintained list of linguistic resources, datasets, and tools specifically for Natural Language Processing (NLP) and Computational Linguistics (CL) on the Spanish language. It aggregates corpora, pre-trained models, annotation tools, and other assets to support research and development involving Spanish text and speech. The project addresses the challenge of discovering high-quality, Spanish-specific NLP resources scattered across the web.
Researchers, data scientists, and developers working on Spanish-language NLP projects, including machine translation, sentiment analysis, speech recognition, and linguistic analysis. It's also valuable for computational linguists and students focusing on Spanish or multilingual NLP.
It saves significant time and effort by centralizing Spanish NLP resources that are otherwise fragmented across academic papers, institutional websites, and various repositories. The list is actively curated and categorized, ensuring quality and relevance for practical use.
Curated list of Linguistic Resources for doing NLP & CL on Spanish
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Aggregates diverse Spanish NLP resources like the Europarl corpus and Freeling tools in one place, saving researchers hours of scattered searching.
Actively maintained through contributions, ensuring updates with new resources such as the Spanish Billion Words Corpus and OSCAR subset.
Organizes resources into clear sections like Speech, NER, and Corpora, making it easy to find specific needs like Mexican Spanish speech databases.
Includes dialect-specific resources, such as South American slang expressions and Mexican speech recognition datasets, addressing regional linguistic diversity.
As a static list, some links may be broken or outdated (e.g., older shared task pages), requiring users to verify each resource's availability independently.
Does not vet the reliability or performance of listed tools like various POS taggers, leaving users to assess suitability for production use on their own.
Provides only links without setup instructions or best practices, increasing the learning curve for implementing tools like ixa-pipe-pos or Sphinx models.