A Universal Part-of-Speech Tagset (Petrov, Das, McDonald)

Abstract

• 12 universal part of speech tags
• mappings from 25 different treebank tagsets used
• Coverage of 22 different languages
• Show grammar induction for predicted part of speech tags using these "universal" tags

Introduction

• Recent interest in unsupervised POS tag induction and cross-lingual projection of POS tags.

Underlying these studies is the idea that a set of (coarse) syntactic POS categories exist in similar forms across languages"

• When corpora that use a standard tagset are not available, typically a mapping from fine-grained tags to a more universal POS tag set is done.
  • Das and Petrov (2011) was an example of this
• Purposes of constructing this tagset:
  • useful for evaluating unsupervised and cross-lingual taggers
  • allows for meaningful comparisons across languages when looking at supervised taggers though the size of the corpus used can still fluctuate, at least the tagset size and distribution is roughly consistent
  • simplifies the development of taggers across multiple languages (less annotation guideline specific information has to be utilized).
• Experiments herein:
  • POS tagging accuracy for 25 different treebanks
  • unsupervised grammar induction system for multiple languages (relying on Das and Petrov (2011) and Naseem et al, (2010).

Tagset

• Adopt a pragmatic focus, trying to find the POS categories that they expect to be most useful for users of POS taggers. - The focus is on utility for downstream tasks and grammar induction tasks

• majority of tagsets are very fine-grained and very language specific

• Smith and Eisner (2005) made a set of 17 English POS tags from the conventional 17 though these did not emphasize the multilingual utility of these tags

• McDonald and Nivre (2007) identified eight different coarse POS tags when analyzing the errors of two dependency parsers on the 13 different languages form the CoNLL shared tasks.

The tags

• NOUN

• VERB

• ADJ

• ADV

• PRON

• DET

• ADP (ADPOSITIONS)

• NUM

• CONJ

• PRT (PARTICLES)

• . (PUNCTUATION)

• X (CATCH ALL)

• we did not rely on intrinsic definitions of the above categories. Instead each category is defined operationally.

  • By this, they mean that they defined these part of speech tags in their relationship to fine-grained POS tags from other treebanks

• Some tags do not occur in all languages Adjectives don't occur in Wolof if I'm remembering that paper correctly

  • For Korean, they treated stative verbs that would translate as adjectives in english as adjectives this seems like a bad, Anglocentric way of doing things.

• One important thing about these mappings is that they were established to encourage collaboraation and refinement from researcheres working on other languages (using version control etc).

• The languages considered are very Indo-European, only 7 of the 25 treebanks are non-IE languages. However, this is probably better than most researchers were doing at the time towards including other non-IE languages

Experiments

POS tagging accuracy comparison

• Model: trigram markov model
  • chosen for speed, state of the art accuracy without much tuning
• Using the universal tags reduced the variance in performance across langs from 10.4 to 5.1.
• Still differences across languages
  • Japanese is very good (99% acc), Turkish worse (90.2% acc)

• The best results are obtained by training on the original fine-graineed tags and then mapping to the UPOS tags at the end

  • The transition model based on the universal POS tagset is less informative

Grammar induction

• Previous research on unsupervised grammar induction assumed gold POS tags. They remove this simplification using POS tags that are automatically projected from English
• Das and Petrov (2011) use cross-lingual projection to lear POS taggers without labeled data the target lang, these induced tags are used to learn unsupervised grammar.
• Using Naseem (2010)'s model where a small set of universal syntactic rules constraina bayesian model I should read that paper if I want to make sense of what was done here
• Using treebanks from the CoNLL-X shared task (eight indoeuropean languages used by Das and Petrov (2011))
• The method described for the grammar induction experiments in this paper are best with the gold UPOS tags performing a little better (though this wasn't the case for all languages examined, swedish for example did better with the automatically generated tags)