Syntactic Annotations for the Google Books NGram Corpus

By Yuri Lin, Jean-Baptiste Michel, Erez Aiden Lieberman, Jon Orwant, Will Brockman, Slav Petrov. 2012

473 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, methodology): “We trained models on the 6 datasets described in Table 1, taken from Google N-Grams (Lin et al., 2012) and the COHA corpus (Davies, 2010).”
HICO: A Benchmark for Recognizing Human-Object Interactions in Images (2015, methodology): “To further expand the coverage of interactions, we also use the Google N-Gram dataset [20], which comes with dependency parsing results, and retrieve the top verbs that precede a particular object name.”
Characterizing the Google Books Corpus: Strong Limits to Inferences of Socio-Cultural and Linguistic Evolution (2015, background): “The second version, published in 2012 [2], contains 8 million books with half a trillion words in English alone, and also includes books in Italian.”
Learning Everything about Anything: Webly-Supervised Visual Concept Learning (2014, methodology): “We specifically use the dependency gram data, which contains parts-of-speech (POS) tagged head=>modifier dependencies between pairs of words, and is much richer than the raw ngram data (see section 4.3 in [30]).”
Temporal Analysis of Language through Neural Language Models (2014, methodology): “mantic-Syntactic Word Relationship test set when trained on the same data. 3.1 Training The Google Books Ngram corpus contains Ngrams from approximately 8 million books, or 6% of all books published (Lin et al., 2012). We sample 10 million 5-grams from the English ﬁction corpus for every year from 1”
Intersecting constraint families: An argument for harmonic grammar (2017, methodology): “We used the Google n-grams corpus for French (Michel et al. 2011, Lin et al. 2012), which includes only sequences that occur at least forty times in the Google Books corpus; we limited results to publications since 1900.”
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge (2013, unknown): “In a second approach to estimating SVO probabilities, we used BerkeleyLM (Pauls and Klein, 2011) to train an n-gram language model on the GoogleNgram corpus (Lin et al., 2012).”
Zipf’s word frequency law in natural language: A critical review and future directions (2014, methodology): “Thanks in large part to the recent availability of gigantic, freely available, longitudinal corpora like Lin et al. (2012), recent studies have also been able to chart changes in word frequencies throughout modern time.”
The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings (2018, background): “The Google Ngram corpus is the product a massive project in text digitization, in collaboration with thousands of the world’s libraries, which distills text from 6% of all books ever published (Lin et al. 2012; Michel et al. 2011).”
How Large Are Lions? Inducing Distributions over Quantitative Attributes (2019, unknown): “Finally, our resource collection is in the line of work that uses counting across very large amounts of data (such as n-grams from books) to produce big resources (Lin et al., 2012; Goldberg and Or-want, 2013), which have had a signiﬁcant impact on NLP Research.”
Can We Generate Emotional Pronunciations for Expressive Speech Synthesis? (2020, unknown): “Word frequencies in French are extracted from Google ngrams [45].”
Statistically Significant Detection of Linguistic Change (2014, methodology): “ the time span from 1900 2005, and set the time snapshot period to 5 years(21 points). We obtain the POS Distribution of each word in the above time range by using the Google Syntactic Ngrams dataset [16, 24, 25]. Amazon Movie Reviews. Amazon Movie Reviews dataset consists of movie reviews from Amaz”