This document discusses natural language processing for Irish and some of the challenges. It provides an overview of NLP and challenges in processing human languages due to their complexity. Machine translation from English to Irish is discussed along with challenges in processing user-generated content in Irish on social media due to unstructured text and features like code-switching. The importance of technology for supporting minority languages is emphasized and examples of applying NLP to social media data in Irish are described.

1. Natural Language Processing
for Irish
Teresa Lynn, PhD
Research Fellow
ADAPT Centre
Dublin City University
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 Overview of Natural Language Processing (NLP)
o English - Irish machine translation
o NLP for User-generated Content
o Importance of technology for Minority Languages

3. www.adaptcentre.ieWhat is Natural Language Processing?
“Using computers to analyse, derive meaning and understand text”
o ‘Attempt’ to understand how humans speak/ use language

4. www.adaptcentre.ieWhat is Natural Language Processing?
“Using computers to analyse, derive meaning and understand text”
o ‘Attempt’ to understand how humans speak/ use language
Why do computers need to understand language?
o Text summarisation
o Sentiment analysis
o Topic extraction (Information Retrieval)
o Grammar Checking
o Text Mining (Big Data problem)
o Machine Translation
o Question-Answering Systems

5. www.adaptcentre.ieChallenges of processing language
• Human languages are:
– Elegant
– Efficient
– Flexible
– Complex
• One word/sentence may mean many things
• Many ways of saying the same thing
• Meaning depends on context
• Literal and figurative language (metaphor)
• Language and culture (different ways of conceptualising
the same thing)

6. www.adaptcentre.ieAmbiguous Headlines
Syntactic ambiguity:
EYE DROPS OFF SHELF
SQUAD HELPS DOG BITE VICTIM
ENRAGED COW INJURES FARMER WITH AXE
STOLEN PAINTING FOUND BY TREE
Semantic Ambiguity
PANDA MATING FAILS; VETERINARIAN TAKES OVER
SAFETY EXPERTS SAY SCHOOL BUS PASSENGERS SHOULD BE BELTED
POLICE BEGIN CAMPAIGN TO RUN DOWN JAYWALKERS
Source: http://www.alta.asn.au/events/altss_w2003_proc/altss/courses/somers/headlines.htm

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

8. www.adaptcentre.ieWhat does a machine know about language?
Sentence = a string/sequence of characters:
“The man saw the boy with the telescope”

9. www.adaptcentre.ieWhat does a machine know about language?
Sentence = a string/ sequence of characters:
“The man saw the boy with the telescope”
Who is doing what? Who has the telescope?

10. www.adaptcentre.ieSyntactic Parsing 101
Who is doing what? Who has the telescope? = Parsing
“The man saw the boy with the telescope”

11. www.adaptcentre.ieSyntactic Parsing 101
Who is doing what? Who has the telescope? = Parsing
“The man saw the boy with the telescope”
DET NOUN VERB DET NOUN PREP DET NOUN
Part-of-speech Tagging

12. www.adaptcentre.ieParsing 101 – ambiguity

13. www.adaptcentre.ieParsing 101 – ambiguity

14. www.adaptcentre.ieTraditional Approach – Rules
‘I like ice-cream in summer’
‘I like summer in ice-cream’ ….??
Syntactic Parsing Rules:
S  NP VP
S  NP VP PP
NP  Noun | Pronoun
VP  Verb NP | Verb PP
PP  Preposition Noun
Noun  ‘ice-cream’ | ‘summer’
Pronoun  `I’
Verb  `like’
Preposition  ‘in’

15. www.adaptcentre.ieMachine Learning – data driven approaches
Supervised Machine Learning requires a LOT of:
• structured data
• annotated data
• reliable data

16. www.adaptcentre.ieMachine Learning – data driven approaches
Supervised Machine Learning requires a LOT of:
• structured data
• annotated data
• reliable data

17. www.adaptcentre.ieMachine Learning – data sparsity
Supervised Machine Learning requires a LOT of:

18. www.adaptcentre.ieMachine Learning – data sparsity
Source: expertsystem.com, redbubble.com

19. www.adaptcentre.ieIrish – Long distance dependencies
VSO: Word Order
English: `The boy who was looking through the
telescope yesterday on the street saw the man’
Irish: Chonaic an buachaill a bhí ag feachaint
tríd an teileascóp inné ar an tsráid an fear sin
Lit Translation [Saw]v [the boy who was looking
through the telescope yesterday on the street]subj [the
man] obj

20. www.adaptcentre.ieIrish Language Features – Sparsity
Word Order
English: `I saw the boy’
Irish: Chonaic mé an buachaill
Translation Saw I the boy

21. www.adaptcentre.ieIrish Language Features

22. www.adaptcentre.ieIrish Language Features
Vowel Harmony
Caithim – `I spend’
Casaim – `I turn’
Rithfinn – `I would run’
D’íosfainn – `I would eat’

23. www.adaptcentre.ieOutline
o Overview of Natural Language Processing (NLP)
o English - Irish machine translation
o NLP for User-generated Content
o Importance of technology for Minority Languages

24. www.adaptcentre.ieUser-Generated Content & NLP
Where do we find UGC?
Blogs
Social Media sites
Micro-blogs (Twitter)
Informal Emails
What is difficult about UGC for NLP?
Unstructured Text
Ungrammatical
Text Speak
Difficult to predict
Various symbols (e.g. Emojis, Hashtags)

25. www.adaptcentre.ieMy Work – Minority Language Twitter
Social Media Bandwagon

26. www.adaptcentre.ieMy Work – Minority Language Twitter
 Code-switching
 Diacritics
 Verb drop
 Spacing issue
 Phonetic spelling
 Abbreviations
grma -> go raibh maith agat
t7ain -> tseachtain

27. www.adaptcentre.ieMy Work – Minority Language Twitter
Goals:
o Build a corpus of POS-tagged Irish tweets
o Train a statistical POS tagger for Irish tweets
o Assess how we can leverage existing resources
o Examine the impact of noisy UG text on existing resources

28. www.adaptcentre.ieCrawled corpus of Irish Tweets

29. www.adaptcentre.iePOS-tagged tweets (with standard POS-tagger)
"<RT>"
"RT" Guess Abr
"<@NiallSF>"
"@NiallSF" Guess Unknown Noun
"<:>"
":" Punct Int
"<Sásta>"
"sásta" Adj Comp
"<go>"
"go" Part Vb Cmpl
"go" Part Vb Subj
"<raibh>"
"bí" Verb VI PastInd Dep Ecl
"bí" Verb VI PresSubj Ecl
"<sé>"
"sé" Pron Pers 3P Sg Masc Sbj
"<suaimhneach>"
"suaimhneach" Adj Base

30. www.adaptcentre.iePOS-tags for Irish Tweets
Adapted from work by Gimpel et al., (2011)

31. www.adaptcentre.ieMapped POS tags
"<RT>"
"RT" ~
"<@NiallSF>"
"@NiallSF" @
"<:>"
":" ~
"<Sásta>"
"sásta" A
"<go>"
"go" T
"<raibh>"
"bí" V
"<sé>"
"sé" O
"<suaimhneach>"
"suaimhneach" A

32. www.adaptcentre.ieApplication of our work
 Sociolinguistic studies
 Improved automated translation of tweets
 Improved sentiment analysis
 Cross-lingual social media analysis

33. www.adaptcentre.ieOutline
o Overview of Natural Language Processing (NLP)
o NLP for UGC
o Irish UGC
o Importance of technology for Minority Languages

34. Image: National Folklore Collection UCD

35. www.adaptcentre.ie
Source:
indigenoustweets.com
Challenging beliefs through technology

36. Source:
indigenoustweets.com

37. www.adaptcentre.ieConclusion
Harness technology to encourage language use:
o at school
o at home (phone technology, games)
o at work (through content creation tools, MT systems)
o online
Influence Government Policy with statistics gathered
through:
o online use analysis
o demand for technology
o empirically demonstrating evolution of language

38. www.adaptcentre.ie
#GRMA
Go Raibh Maith Agaibh
Thank you!
Questions?
Contact: teresa.lynn@adaptcentre.ie

39. www.adaptcentre.ieLanguage at Risk
Need to ensure continuing language usage
…….through technology
o Edutainment packages
o Word processing tools
o Webpage translation
o Search engines
o Games
o Social media
o Summarise discussions
o Monitor user sentiment
o Track misuse
Source: http://www.leuphana.de/institute/ies/llt2015.html