In NLP, languages are often referred as low resource or high resource.

What do these terms mean?


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High resource languages are languages for which many data resources exist, making possible the development of machine-learning based systems for these languages. English is by far the most well resourced language. West-Europe languages are quite well covered, as well as Japanese and Chinese. Naturally low-resource languages are the opposite, that is languages with none or very few resources available. This is the case for some extinct or near-extinct languages and many local dialects. There are actually many languages which are mostly oral, for which very few written resources exist (let alone resources in electronic format); for some there are written documents but not even something as basic as a dictionary.

There are many different types of resources which are needed in order to train good language-based systems:

  • a high amount of raw text from various genres (type of documents), e.g. books, scientific papers, emails, social media content, etc.
  • lexical, syntactic and semantic resources such as dictionaries, dependency tree corpora, semantic databases (e.g. WordNet), etc.
  • task-specific resources such as parallel corpora for machine translation, various kinds of annotated text (e.g. with part-of-speech tags, named entities, etc.)

Many types of language resources are costly to produce, this is why the economic inequalities between countries/languages are reflected in the amount (or absence) of language resources. The Universal Dependencies project is an interesting effort to fill this gap.


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