{
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    "https://repo.anaconda.com/pkgs/main"
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  "conda_build_version": "3.21.4",
  "conda_private": false,
  "conda_version": "4.10.3",
  "env_vars": {
    "CIO_TEST": "<not set>"
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  "extra": {
    "copy_test_source_files": true,
    "final": true,
    "recipe-maintainers": [
      "conda-forge/r"
    ]
  },
  "home": "https://bnosac.github.io/udpipe/en/index.html, https://github.com/bnosac/udpipe",
  "identifiers": [],
  "keywords": [],
  "license": "MPL-2.0",
  "license_family": "OTHER",
  "license_file": "LICENSE",
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  ],
  "summary": "This natural language processing toolkit provides language-agnostic 'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency parsing' of raw text. Next to text parsing, the package also allows you to train annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.",
  "tags": []
}