---
title: "A Dataset of Syntactic-Ngrams over Time from a Very Large"
description: "The paper offers no new method. 30 references, cited by 60 papers here. Among them: 131."
authors: ["Yoav Goldberg", "Jon Orwant"]
year: 2013
venue: "Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity"
source: "https://aclanthology.org/S13-1035/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goldberg-2013-dataset-syntactic-ngrams-over-time/"
---
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# A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books

**Authors:** [Yoav Goldberg](https://md.research.elamur.ai/free/en/authors/yoav-goldberg/) and Jon Orwant · **Year:** 2013 · **Venue:** [Second Joint Conference on Lexical and Computational Semantics (\*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity](https://md.research.elamur.ai/free/en/venues/joint-conference-on-lexical-and-computational-semantics-sem-proceedings-of-the-2/) ([Joint Conference on Lexical and Computational Semantics (\*SEM), Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity 2013](https://md.research.elamur.ai/free/en/venues/joint-conference-on-lexical-and-computational-semantics-sem-proceedings-of-the-2/2013/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/S13-1035/) · [PDF](https://aclanthology.org/S13-1035.pdf) · [OpenAlex](https://openalex.org/W2250837944)

**Citations:** 131 citations (OpenAlex, 2026-10-14) · [60](https://md.research.elamur.ai/free/en/papers/goldberg-2013-dataset-syntactic-ngrams-over-time/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Large corpora are widely used to learn word properties from context, but syntactic counts at web scale were hard to obtain, and studies seldom shared a common corpus. The authors built a standard, reusable resource by parsing the English Google Books collection: about 3.5 million books, 345 billion tokens, published from 1520 to 2008. They tagged the text with a CRF tagger, parsed it into basic Stanford dependencies with a beam-search shift-reduce parser, and counted tree fragments called syntactic-ngrams. These fragments are defined by how many content words they contain, not by their position in the linear string. The released dataset has more than 10 billion distinct fragments. Each one comes with a total count and per-year counts, plus counts for subcorpora such as Fiction, British, American and a uniform 1M-book sample. The paper offers no new method. Its value is a free, large, temporally indexed set of counts that supports work on selectional preferences, subcategorization, syntactic language modeling and change in word meaning, without anyone having to rerun parsing at this scale.

## Key points
- **Source corpus:** English Google Books, 3,473,595 books, 17.6B sentences and 345.1B tokens.
  - Pages with more than 600 white-spaces and sentences longer than 60 tokens were dropped.
  - A fragment is kept only if it occurs at least 10 times in the whole corpus.
- **Processing pipeline:**
  - Penn Treebank POS tags from a first-order CRF tagger. It was trained on WSJ, Brown and the Questions Treebank and uses word-cluster features derived from Books trigrams.
  - A reimplemented Zhang & Clark shift-reduce parser with beam size 8 and Zhang & Nivre features, trained with 4-way jack-knifing.
  - Output follows the basic variant of Stanford dependencies.
- **Fragment sets:**
  - nodes (47M), arcs (919M), biarcs (1.78B) and triarcs (1.87B), plus larger quadarcs.
  - "extended-*" versions that also keep functional markers.
  - verbargs (130M) and nounargs (275M), each with an unlexicalized variant that keeps only the head and the top-1000 words.
- **Temporal example:** comparing the syntactic contexts of "rock" year by year shows it moving toward "jazz" around 1968.
- **Release:** the data is free under CC BY-NC-SA 3.0. The authors deliberately report no parsing accuracy figure, arguing that a single number would mislead on such a heterogeneous corpus.

## Interesting ideas
- Fragment size is measured in content words only, and determiners, auxiliaries, negation and similar words are attached as optional "functional markers" in separate extended sets; this keeps the counts focused on meaning while still letting users recover polarity, modality and definiteness.
- Prepositions and coordinators stay as tree nodes rather than being folded into labels like prep_with; this preserves where they occur, still works when one side is missing (as in "hard to deal with"), and keeps the label inventory small.
- Verb-argument patterns are unlexicalized by replacing every word outside the top-1000 with a *W* placeholder, so that rare but structurally interesting frames survive the frequency cutoff.
- Counts are split by year, and similarity between a word's syntactic contexts is tracked over time to spot when a new sense appears.
- The parser is trained on jack-knifed predicted POS tags, and the authors refuse to publish one accuracy number for mixed-genre, mixed-OCR data.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): frames could be keyed on content-word arguments, with negation, modality and definiteness stored as attached features instead of as separate slots.
- **I2** (tree): when converting between UD dialects, keeping case/cc words as nodes avoids an explosion of labels and handles stranded prepositions consistently.
- **I3** (world): compiled subcategorization knowledge for verb classes could be built from such partly abstracted frames, so rare verbs still get usable argument patterns.
- **I4** (world): date-sliced context profiles could flag archaic or shifted senses when reading older fables and tales, helping pick the right WordNet sense.
- **I5** (learn): train the parser on tags from the en tagger rather than gold tags, and report silver-data quality per genre or slice instead of as a single overall score.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 353 works · 364,806 citations
- [Bar-Ilan University](https://md.research.elamur.ai/free/en/universities/bar-ilan-university/) — University, Ramat Gan, Israel · 118 works · 21,077 citations

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🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works

## Cite

Cite as: Yoav Goldberg and Jon Orwant. 2013. A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books. Second Joint Conference on Lexical and Computational Semantics (\*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{goldberg2013dataset,
  title = {{A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books}},
  author = {Yoav Goldberg and Jon Orwant},
  booktitle = {Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity},
  year = {2013},
  url = {https://aclanthology.org/S13-1035/}
}
```

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30 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*