Dataset Open Access

Lemmatized English Word2Vec data

Christian Chiarcos; Tomas Mikolov et al.

# 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 https://code.google.com/archive/p/word2vec/ under an Apache License 2.0. We obtained the Word2vec data from  https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing 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.

Partially funded by the German Federal Ministry of Education and Research (BMBF), project "Linked Open Dictionaries".
Files (4.1 GB)
Name Size
GoogleNews-vectors-negative300.bin.gz
md5:1c892c4707a8a1a508b01a01735c0339
1.6 GB Download
GoogleNews-vectors-negative300.txt_ADJ.csv.gz
md5:999cb5e1b5f5e4a455a7546aec6d5f7b
10.8 MB Download
GoogleNews-vectors-negative300.txt_ADJ.lemmas
md5:7e3e99e45f8f4ae366576e31b9fc49c4
242.8 kB Download
GoogleNews-vectors-negative300.txt_ADV.csv.gz
md5:65f90c77d7146c6d4a543228ade30ffa
4.3 MB Download
GoogleNews-vectors-negative300.txt_ADV.lemmas
md5:22b97495119c703a144cbe154dd72c31
108.8 kB Download
GoogleNews-vectors-negative300.txt_NOUN.csv.gz
md5:e07c9befabc0bbc2dc63027d8a3f0303
309.5 MB Download
GoogleNews-vectors-negative300.txt_NOUN.lemmas
md5:2bcb8f51b665986c36251face380807c
6.3 MB Download
GoogleNews-vectors-negative300.txt_OTHER.csv.gz
md5:39d0f28cc010daf0350f8d54c8d5486c
443.8 MB Download
GoogleNews-vectors-negative300.txt_OTHER.lemmas
md5:e3c02ad8f9a0fdbdfe5c464f1b266453
21.9 kB Download
GoogleNews-vectors-negative300.txt_RARE.csv.gz
md5:3861e08f3ac52d410fb7698d09eea0b8
811.6 MB Download
GoogleNews-vectors-negative300.txt_VERB.csv.gz
md5:df34194305a427876235a342592aa899
38.6 MB Download
GoogleNews-vectors-negative300.txt_VERB.lemmas
md5:1fc2e14f0f59791da5e1760fb0e0a888
775.3 kB Download
GoogleNews-vectors-negative300.txt_VERY_RARE.csv.gz
md5:41333adfcdbe13138ab03b20cdbc4c64
824.6 MB Download
LICENSE
md5:3b83ef96387f14655fc854ddc3c6bd57
11.4 kB Download
LICENSE.wordnet
md5:bc01d92eb6af5635c4038bec7ec833e1
1.6 kB Download
README.md
md5:9126c091a774036afc13c4453276c55b
2.0 kB Download
README.word2vec
md5:6a79261a4c4572d7ef150173b5c381e8
1.2 kB Download
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