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Lemmatized English Word2Vec data

Christian Chiarcos; Tomas Mikolov et al.

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Christian Chiarcos</dc:creator>
  <dc:creator>Tomas Mikolov et al.</dc:creator>
  <dc:description># Lemmatized English Word2Vec data

This is a version of the original GoogleNews-vectors-negative300 Word2Vec embeddings for English.
In addition, we provide the following modified files:

- converted to conventional CSV format (and gzipped)
- subclassified:
  for the most frequent 1.000.000 words:
    subclassified according to WordNet parts of speech: ADJ, ADV, NOUN, VERB, OTHER
    note that one embedding can be associated with multiple parts of speech
  for the remaining words:
    RARE: top 1.000.001 - 2.000.000 words
    VERY_RARE: top 2.000.001 - 3.000.000 words
- WordNet lemmatization (via NLTK) in separate files
    (first lemma only)

Note that this is not a product of original research, but a derived work, deposited here as a point of permanent reference and as a building stone of subsequent research. For such application, a publication independent from Google is necessary to guarantee stability against changes in their data releases.

The original Word2vec code and data was published via under an Apache License 2.0. We obtained the Word2vec data from on Jun 3, 2020.

The Word2vec documentation included the following references:

    [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.

    [2] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.

    [3] Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of NAACL HLT, 2013.

The derived data is made available under the same license (Apache License 2.0). However, note that the content derived from WordNet (lemmas) are subject to the Princeton Wordnet license as stated in LICENSE.wordnet.

Data provided by the Applied Computational Linguistics Lab of the Goethe University Frankfurt, Germany. Original data developed by Mikolov et al.</dc:description>
  <dc:description>Partially funded by the German Federal Ministry of Education and Research (BMBF), project "Linked Open Dictionaries".</dc:description>
  <dc:subject>word embeddings</dc:subject>
  <dc:title>Lemmatized English Word2Vec data</dc:title>
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