This is a talk I gave at the Dublin NLP Meetup Group in July 2017 - It gives an introduction to parsing and treebanks before moving onto describing the Universal Dependencies Project (which includes a mapping of the Irish Dependency Treebank)

1. Universal Dependencies
Project
Teresa Lynn,
Postdoctoral Researcher, DCU
The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

2. www.adaptcentre.ieOutline
o Supervised Machine Learning: Data Sets
o Parsing 101
o Treebanks
o Universal Dependencies Project

3. www.adaptcentre.ieMachine Learning – on data sets
Supervised Machine Learning requires:
• structured data
• annotated data
• reliable data

4. www.adaptcentre.ieWhat does a machine know about language?

5. www.adaptcentre.ieWhat does a machine know about language?

6. www.adaptcentre.ieParsing = who is doing what?
Sentence = a string/sequence of characters:
“True self-control is waiting until the movie starts to eat your popcorn”

7. www.adaptcentre.ieParsing = who is doing what?
Sentence = a string/sequence of characters:
“True self-control is waiting until the movie starts to eat your popcorn”

8. www.adaptcentre.ieParsing = who is doing what?
Sentence = a string/sequence of characters:
“True self-control is waiting until the movie starts to eat your popcorn”
“True self-control is waiting until the movie starts to eat your popcorn”

9. www.adaptcentre.ieParsing = who is doing what?
Sentence = a string/sequence of characters:
“True self-control is waiting until the movie starts to eat your popcorn”
“True self-control is waiting until the movie starts to eat your popcorn”
“True self-control is waiting until the movie starts to eat your popcorn”

10. www.adaptcentre.ieParsing = who is doing what?
Sentence = a string/sequence of characters:
“True self-control is waiting until the movie starts to eat your popcorn”
“True self-control is waiting until the movie starts to eat your popcorn”
“True self-control is waiting until the movie starts to eat your popcorn”
“True self-control is waiting until the movie starts to eat your popcorn”

11. www.adaptcentre.ieString or sequence of characters
“Others have tried to spruce up frequent-flier programs”

12. www.adaptcentre.iePart-of-speech tagging
Others have tried to spruce up frequent-flier programs
NNS VBP VBN TO VB RP JJ NNS

13. www.adaptcentre.ieSyntactic Parsing – Phrase Structure tree

14. www.adaptcentre.ieProblems with Constituency parsing
• Tied to linguistic theories such as transformational grammar
• Led by English-speaking linguists with English data (e.g. Chomsky)
• Word order is central to constituency grammars
• No close links to semantics

15. www.adaptcentre.ieProblems with Constituency parsing
• Tied to linguistic theories such as transformational grammar
• Led by English-speaking linguists with English data (e.g. Chomsky)
• Word order is central to constituency grammars
• No close links to semantics

16. www.adaptcentre.ieSyntactic Parsing - Dependency trees
“We have already discussed this question at some length.”

17. www.adaptcentre.ieSyntactic Parsing - Dependency trees
“Others have tried to spruce up frequent-flier programs”

18. www.adaptcentre.ieAdvantages of Dependency parsing
• Better handling of free word order (less-Anglo-centric)
• Node simplicity
• Clean mapping to semantic predicate-argument structure
• Easier to develop multilingual systems

19. www.adaptcentre.ieWhat does a machine know about language?
Sentence = a string/sequence of characters:
“True self-control is waiting until the movie starts to eat your popcorn”

20. www.adaptcentre.ieParsing = who is doing what?
“True self-control is waiting until the movie starts to eat your popcorn”
You are waiting
You will eat your popcorn

21. www.adaptcentre.ieParsing = who is doing what?
“True self-control is waiting until the movie starts to eat your popcorn”
True self-control is waiting
The movie will eat your popcorn

22. www.adaptcentre.ieTreebanks
• Collection of parsed sentences (trees)
• Annotated with a pre-defined part-of-speech tagset (Noun, Verb, etc)
• Pre-defined annotation scheme
(list of prescribed labels)
• Pre-defined linguistic structure
• Used to develop statistical parsers (train, test, and bootstrap)

23. www.adaptcentre.ieDependency Treebanks – variations
Varying labelling conventions:

24. www.adaptcentre.ieDependency Treebanks – variations
Varying labelling conventions:

25. www.adaptcentre.ieDependency Treebanks – variations
Varying structural analyses:

26. www.adaptcentre.ieDependency Treebanks – variations
Varying structural analyses:

27. www.adaptcentre.ieTreebanks – variations
Problems with variations:
• Difficult to do cross-lingual analysis
• Difficult to compare parser performance
• Difficult to do cross-lingual transfer
(using data from one language to help another)
• Difficult to build and evaluate multilingual systems

28. www.adaptcentre.ieSolution: Universal Dependencies Project
Premise:
no Universal Grammar, but:
“all languages share fundamental similarities” (linguistic universals)
Goals:
• develop a set of harmonised dependency treebanks
• design a universal annotation scheme
• enable comparison of treebanks
• enable comparison of parsing results
• improve multilingual processing

29. www.adaptcentre.ieSolution: Universal Dependencies Project
Slide credit: Chris Manning, Stanford University

30. www.adaptcentre.ieSolution: Universal Dependencies Project
Slide credit: Chris Manning, Stanford University

31. www.adaptcentre.iePart-of-Speech Tags
Taxonomy of 17 universal part-of-speech tags, expanding on the Google
Universal Tagset (Petrov et al., 2012)
All languages use the same inventory, but not all tags have to be used by
all languages
Open Closed Other
ADJ ADP PUNCT
ADV AUX SYM
INTJ CCONJ X
NOUN DET
PROPN NUM
VERB PART
PRON
SCONJ
Slide credit: Chris Manning, Stanford University

32. www.adaptcentre.ieSyntax
• Content words are related by dependency relations
• Function words attach to the content word they further specify
• Punctuation attaches to head of phrase or clause
Slide credit: Chris Manning, Stanford University

33. www.adaptcentre.ieSubstantive parallelism achieved
Slide credit: Chris Manning, Stanford University

34. www.adaptcentre.ieDependency Relations
• 40 universal grammatical relations (de Marneffe et al., 2014)
(aim to address linguistic universals across languages)
• Language-specific subtypes may be added
(e.g. Irish UD: csubj:cleft)

35. www.adaptcentre.ie
• Standardized inventory of morphological features, based on the
Interset system (Zeman, 2008)
• Languages select relevant features and can add language-specific
features or values with documentation
Lexical
Inflectional
Nominal
Inflectional
Verbal
PronType Gender VerbForm
NumType Animacy Mood
Poss Number Tense
Reflex Case Aspect
Definite Voice
Degree Person
Polarity
Features
Slide credit: Chris Manning, Stanford University

36. www.adaptcentre.ieFeatures
Slide credit: Chris Manning, Stanford University

37. www.adaptcentre.ieTimeline of UD project to date
Release of annotation guidelines (v1): October 2014
• 10 treebanks: January 2015
• 18 treebanks: May 2015
• 37 treebanks: November 2015
• 54 treebanks: May 2016
• 64 treebanks: November 2016
Release of annotation guidelines (v2): December 2016
• 70 treebanks (50 languages) : March 2017

38. www.adaptcentre.ieuniversaldependencies.org

39. www.adaptcentre.ieGoogle Parser

40. www.adaptcentre.ie
#GRMA
Go Raibh Maith Agaibh
Ceisteanna?
http://universaldependencies.org
https://github.com/UniversalDependencies