wordscommon

wordscommon is a free, open-source lexical resource that compiles a curated list of the most frequent words across multiple languages for use in natural language processing, education, and information retrieval. The project aims to provide a simple, language-aware baseline vocabulary that can be deployed in NLP pipelines, text simplification, readability assessment, and language learning tools. The resource emphasizes high-frequency lexemes rather than exhaustive lexicons, enabling faster tokenization, vocabulary modeling, and efficient indexing.

Structure and data model: Each language entry in wordscommon includes a lemma, a part of speech tag, a frequency rank or count, and optional metadata such as language variety (American vs British English) or semantic domains. Some versions also include multiword expressions and basic inflection notes. The data are typically distributed as plain text lists or compact JSON/CSV files with versioned releases.

Origins and development: wordscommon emerged from collaborative efforts in the open data and NLP communities to standardize high-frequency vocabulary for cross-project compatibility. It is maintained by volunteers and institutions and is released under an open license, with periodic updates reflecting new corpus data. The resource is designed to be language-agnostic where possible, with separate lists for each supported language and cross-language mappings to aid bilingual applications.

Applications and limitations: Common word lists like wordscommon are widely used for tokenizer calibration, stop-word handling, readability metrics, and initial vocabulary bootstrapping in language models. They may introduce bias toward the corpora used for frequency estimation and may not cover domain-specific terms or less-resourced languages equally.

See also: stop words, frequency dictionaries, lexical databases, natural language processing resources.