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This page pertains to UD version 2.

UD English PUD

Language: English (code: en)
Family: Indo-European, Germanic

This treebank has been part of Universal Dependencies since the UD v2.1 release.

The following people have contributed to making this treebank part of UD: Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Slav Petrov, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, Jesse Kirchner, Lorenzo Lambertino, Martin Popel, Daniel Zeman, Christopher Manning, Sebastian Schuster, Siva Reddy.

Repository: UD_English-PUD
Search this treebank on-line: PML-TQ

License: CC BY-SA 3.0

Genre: news, wiki

Questions, comments? General annotation questions (either English-specific or cross-linguistic) can be raised in the main UD issue tracker. You can report bugs in this treebank in the treebank-specific issue tracker on Github. If you want to collaborate, please contact [syntacticdependencies (æt) lists • stanford • edu]. Development of the treebank happens outside the UD repository. If there are bugs, either the original data source or the conversion procedure must be fixed. Do not submit pull requests against the UD repository.

Annotation Source
Lemmas assigned by a program, not checked manually
UPOS annotated manually, natively in UD style
XPOS annotated manually
Features assigned by a program, not checked manually
Relations annotated manually, natively in UD style


This is the English portion of the Parallel Universal Dependencies (PUD) treebanks created for the CoNLL 2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies (http://universaldependencies.org/conll17/).

This is a part of the Parallel Universal Dependencies (PUD) treebanks created for the CoNLL 2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies (http://universaldependencies.org/conll17/). There are 1000 sentences in each language, always in the same order. (The sentence alignment is 1-1 but occasionally a sentence-level segment actually consists of two real sentences.) The sentences are taken from the news domain (sentence id starts in ‘n’) and from Wikipedia (sentence id starts with ‘w’). There are usually only a few sentences from each document, selected randomly, not necessarily adjacent. The digits on the second and third position in the sentence ids encode the original language of the sentence. The first 750 sentences are originally English (01). The remaining 250 sentences are originally German (02), French (03), Italian (04) or Spanish (05) and they were translated to other languages via English. Translation into German, French, Italian, Spanish, Arabic, Hindi, Chinese, Indonesian, Japanese, Korean, Portuguese, Russian, Thai and Turkish has been provided by DFKI and performed (except for German) by professional translators. Then the data has been annotated morphologically and syntactically by Google according to Google universal annotation guidelines; finally, it has been converted by members of the UD community to UD v2 guidelines. Martin Popel automatically converted the data to UD v2 and Sebastian Schuster, Siva Reddy, and Christopher Manning manually corrected UPOS tags and syntactic annotations. Morphological features and lemmata were added automatically using Stanford CoreNLP.

Additional languages have been provided (both translation and native UD v2 annotation) by other teams: Czech by Charles University, Finnish by University of Turku and Swedish by Uppsala University.

The entire treebank is labeled as test set (and was used for testing in the shared task). If it is used for training in future research, the users should employ ten-fold cross-validation.


The sentences were provided by DKFI, and the 250 sentences in German, French, Italian, and Spanish were translated to English by professional translators.

Syntactic and morphological annotations were originally added by Google according to Google universal annotation guidelines, then automatically converted to UD v2 by Martin Popel, and finally manually corrected by Sebastian Schuster, Siva Reddy, and Christopher Manning. Morphological features and lemmata were added by Sebastian Schuster.

Statistics of UD English PUD

POS Tags






Tokenization and Word Segmentation



Nominal Features

  • Gender
    • Fem
      • PRON: her, she, herself
    • Masc
      • PRON: he, his, him, himself
    • Neut
      • PRON: it, its, itself
  • Number
    • Plur
      • DET: these, those
      • NOUN: people, years, police, investors, months, companies, countries, euros, films, children
      • NUM: 2000s
      • PRON: their, they, we, them, our, us, those, themselves, these
      • PROPN: States, Alps, Powers, Ages, Americans, Americas, Andes, Balkans, Chinese, Democrats
    • Sing
      • ADJ: Canadian, Historian, Spanish, male
      • ADV: course
      • AUX-Fin: was, is, has, 's, ’s, does, am, means
      • DET: this, that
      • NOUN: time, year, government, city, state, war, century, world, day, place
      • PRON: it, he, his, I, her, its, she, this, him, that
      • PROPN: China, Sea, October, Trump, North, America, April, Europe, France, War
      • SYM: %
      • VERB: has, is, was, says, 's, makes, helps, seems, working, appears
      • VERB-Fin: has, is, was, says, 's, makes, helps, seems, appears, contains
  • Case
    • Acc
      • PRON: it, him, them, her, us, me, himself, themselves, itself, You
    • Nom
      • PRON: it, he, they, I, she, we, you
  • Definite
    • Def
      • DET: the
    • Ind
      • DET: a, an
  • Degree and Polarity

  • Degree
    • Cmp
      • ADJ: more, greater, higher, better, earlier, fewer, lower, Elder, Stranger, bigger
      • ADV: longer, earlier, less, better, closer, further
    • Pos
      • ADJ: new, many, other, such, last, great, high, first, own, several
      • ADV: well, far, late, soon, close, early, hard, long, Fast
    • Sup
      • ADJ: best, most, biggest, latest, worst, tallest, largest, least, deepest, earliest
      • ADV: least, best
  • Polarity
    • Neg
      • ADV: not, no, n’t, never, n't
      • DET: no
      • PART: not, n't, n’t
  • Verbal Features

  • Mood
    • Ind
      • AUX-Fin: was, is, are, were, has, had, have, 's, did, do
      • NOUN-Fin: hit
      • VERB-Fin: said, took, are, has, is, began, was, became, says, have
  • Tense
    • Past
      • AUX-Fin: was, were, had, did, got
      • AUX-Part: been, become, declared, frozen, named
      • NOUN-Fin: hit
      • VERB-Fin: said, took, began, was, became, had, told, worked, were, allowed
      • VERB-Part: used, known, given, made, built, left, released, seen, allowed, considered
    • Pres
      • AUX-Fin: is, are, has, have, 's, do, ’s, does, am, ’m
      • VERB-Fin: are, has, is, says, have, 's, include, makes, helps, say
      • VERB-Part: investigating, seeking, thinking, attending, boarding, bracing, calling, carrying, celebrating, coming
  • Pronouns, Determiners, Quantifiers

  • PronType
    • Art
      • DET: the, a, an
    • Dem
      • ADV: then, there, here
      • DET: this, that, these, those
      • PRON: this, that, those, these
      • SCONJ: that
    • Int
      • ADV: when, how, why, where, whenever
      • DET: whatever, which
      • PRON: what, which, who, whose, where, whoever, whom
      • SCONJ: when
    • Prs
      • PRON: it, he, his, their, they, I, her, its, she, we
    • Rel
      • ADV: where, when, why
      • DET: that, which
      • PRON: which, that, who
      • SCONJ: that
  • NumType
    • Card
      • NUM: one, two, three, million, 10, four, 1, six, 3, 2014
      • PROPN: I, I., V, VI, X
    • Mult
      • ADV: once, twice
    • Ord
      • ADJ: first, second, third, 8th, 16th, 20th, 3rd, 5th, 13th, 14th
  • Poss
    • Yes
      • PRON: his, their, its, her, our, my, whose, your
  • Reflex
    • Yes
      • PRON: himself, themselves, itself
  • Person
    • 1
      • AUX-Fin: am
      • PRON: I, we, our, my, us, me
    • 2
      • PRON: you, your
    • 3
      • AUX-Fin: was, is, has, 's, ’s, does, means
      • PRON: it, he, his, their, they, her, its, she, him, them
      • VERB-Fin: has, is, was, says, 's, makes, helps, seems, appears, contains
  • Other Features

  • Foreign
    • Yes
      • X: de, Andes, Force, coup, tipo
  • Syntax

    Auxiliary Verbs and Copula

    Core Arguments, Oblique Arguments and Adjuncts

    Here we consider only relations between verbs (parent) and nouns or pronouns (child).

    Verbs with Reflexive Core Objects

    Relations Overview