Language identification is a crucial component in the automated production of
language resources, particularly in multilingual and big data contexts.
However, commonly used language identifiers struggle to differentiate between
similar or closely-related languages. This paper introduces FastSpell, a
language identifier that combines fastText (a pre-trained language identifier
tool) and Hunspell (a spell checker) with the aim of having a refined
second-opinion before deciding which language should be assigned to a text. We
provide a description of the FastSpell algorithm along with an explanation on
how to use and configure it. To that end, we motivate the need of such a tool
and present a benchmark including some popular language identifiers evaluated
during the development of FastSpell. We show how FastSpell is useful not only
to improve identification of similar languages, but also to identify new ones
ignored by other tools.

本文介绍了 FastSpell，这是一种语言识别工具，它结合了 fastText 和 Hunspell，并提供了细化的第二意见，以便在决定将哪种语言分配给一段文本之前使用。我们描述了 FastSpell 算法的工作原理和配置方法，并在开发 FastSpell 过程中对一些常用语言识别工具进行了评估。我们展示了 FastSpell 的用途不仅在于提高对类似语言的识别，还在于识别其他工具忽略的新语言。