1. Talp Research Center, UPC, Barcelona, Spain
Arabic WordNet: What has been done, what could we do, what should we do?
Horacio Rodríguez
Talp Research Center, UPC, Barcelona, Spain
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2. Index of the talk Introduction Arabic WordNet Ontologies Wordnets
What has been done
what could we do
what should we do
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3. Introduction semantic components used in NLP applications:
Ontologies
large-scale knowledge-bases.
Need (or convenience) of developing
wide-coverage
domain-independent
lexico-conceptual ontologies
WordNet
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4. Ontologies
Ontologies represent static domain knowledge allowing an efficient use by multiple knowledge agents
Acquiring domain knowledge for building ontologies is highly costly and time consuming.
For this reason lots of methods and techniques have been developed for trying to reduce such efforts
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5. Ontologies What an ontology is: Studer et al, 1998
an ontology is a formal explicit specification of a shared conceptualization
Gruber, 1993
Studer et al, 1998
A conceptualization is an abstract, simplified view of the world represented for some purpose
An ontology is a description (formal specification) of a set of concepts and relationships for enabling knowledge sharing and reuse (to perform logical commitments)
An ontology commitment is an agreement to use a vocabulary in a way that is consistent with respect to the theory specified by the ontology
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6. Lexico-Conceptual Ontologies
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7. Ontologies
The mapping between lexical items (words or multiwords) and concepts can be complex.
Due to polysemy, most lexical items can be mapped into more than one concept.
Due to synonymy, more than one word can be mapped to a concept.
Usually the mapping is splitted into two steps
from words into word-senses (i.e. different word meanings)
and from word-senses into concepts.
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8. Wordnets Princeton's English WordNet
(Miller et al, 1990), (Fellbaum, 1998)
Semantic Information
more than 123,000 words organised in 117,000 synsets (WN3.0)
more than 235,000 relations between synsets
Freely available:
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9. Wordnets Princeton's English WordNet
Lexicalised concepts (words, compounds, multiwords)
Synset: synonym set (of words)
Large semantic net conecting synsets
synonymy, antonymy, hyperonymy, hyponymy, meronymy, implication, causation ...
Structure
Noun hierarchy depth ~12
Verb hierarchy depth ~3
Adjective/adverb not in hierarchy, but in star structure
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10. Exemple of WN relations
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11. Wordnets
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12. Wordnets Beyond WN EuroWordNet (Vossen 98) UE funded project
Integrated local wordnets in several languages
English Sheffield
Dutch Amsterdam
Italian Pisa
Spanish UB, UPC, UNED.
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13. Wordnets
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14. Wordnets EuroWordNet Architecture Core Extensions
Inter-Lingual-Index (ILI)
Top Concept Ontology (TCO)
Domain Ontology (DO)
Extensions
Local wordnets
Domain wordnets
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15. Wordnets Beyond WN EWN2 ITEM, CREL EuroTerm, Jur-Wordnet Balkanet
German (GermaNet), French, Chec, Swedish, Estonian
ITEM, CREL
Spanish, Catalan, Basque (UB, UPC)
EuroTerm, Jur-Wordnet
Extending EWN in particular domain
Balkanet
Extending EWN for the Balkan languages
Hownet
Chinese WN
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16. Wordnets
Macro Ontologies based on WN
MCR
Yago
Omega
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17. Arabic WordNet USA REFLEX program funded (2005-2007) Partners:
Universities
Princeton
Manchester
UPC (Barcelona)
UB (Barcelona)
Companies
Articulate Software
Irion
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18. Arabic WordNet papers Introducing the Arabic WordNet Project
Black et al, 2006
Building a WordNet for Arabic
Elkateb et al, 2006
Arabic WordNet: Current State and Future Extensions
Rodríguez et al, 2008
Arabic WordNet: Semi-automatic Extensions using Bayesian Inference
Automatically Extending NE coverage of Arabic WordNet using Wikipedia
AlKhalifa, Rodríguez, 2009
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19. Arabic WordNet Objectives
10,000 synsets including some amount of domain specific data
linked to PWN 2.0
finally to PWN 3.0
linked to SUMO
+ 1,000 NE
manually built (or revised)
vowelized entries
including root of each entry
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20. Arabic WordNet Criteria for selecting synsets to be covered
Connectivity
as densely connected as possible
Most of them connected to English WN counterparts
the overall topology of both wordnets is expected to be similar.
Relevance
Frequent and salient concepts
Generality
Synsets on the highest levels of WN
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21. Arabic WordNet Approach described in 3rd GWC (Elkateb et al, 2006)
Manually built
2 lexicographic interfaces
Manchester, Barcelona
guided by automatically generated suggestions of <Arabic word, English synset> pairs coming from bilingual resources.
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22. Arabic WordNet Approach BCs Filling gaps
Covering of EWN & Balkanet Base Concepts
Filling gaps
Building Arabic specific synsets
Covering domain specific synsets
Adding NEs.
(Semi) automatic extensions
heuristic based
Bayesian networks
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23. Arabic WordNet Resources used LOGOS database of Arabic verbs:
contains 944 fully conjugated Arabic verbs
Bilingual (Arabic-English) dictionaries
NMSU bilingual Arabic-English lexicon:
Salmoné
University of Barcelona
Effel
Corpora
Arabic GigaWord Corpus (from LDC)
UN ( ) bilingual Arabic-English Corpus (from LDC).
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24. Arabic WordNet Representation database (implemented in MySQL)
interchange format (XML)
The database structure comprises four principal entity types:
item, word, form and link.
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25. AWN: What has been done Current (Final ?, we hope no!!!) figures
up to date statistics:
Arabic synsets
11270
Arabic words
23496
pos
DB content a
661 n
7961 r
110 v
2538
Named entities:
Synsets that are named entities
1142
Synsets that are not named entities
10028
Words in synsets that are named entities
1656
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26. AWN: What has been done Software Lexicographer's Web Interface
User's Web Interface
The Arabic Word Spotter
AWN browser
AWN to SUMO mapping including automatic generation of Arabic paraphrases of SUMO formal axioms
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27. AWN: What could we do
AWN has a relatively small coverage compared with PWN
But due to the way of building it, the coverage of most important concepts is comparable.
AWN has a lower density of relations compared with PWN
But many uses of PWN are reduced to the hypernymy/hyponymy relation and the coverage of this relation is similar in both WNs
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28. AWN: What could we do
AWN is fully linked to PWN and through PWN to many other WNs
Existing WNs, specially PWN are used today in almost all NLP tasks that need (or involve) semantic (lexical) knowledge
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29. AWN: What could we do USE AWN FOR NLP TASKS So, the morale is:
For instance:
Look at Csomai's bibliography in WN page
NLP tasks
IR, IE, MT, WSD, Coreference Resolution, Summarization, Textual Entailment, WN mappings, NER, NERC, Language Models, Semantic distances
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30. AWN: What should we do IMPROVE AND EXTEND AWN
AWN is far to be complete
only 10,000 regular synsets
only 1,000 NE synsets
low density of relations
lack of appropriate APIs for interfacing computer applications
So, the morale is:
IMPROVE AND EXTEND AWN
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31. AWN: What should we do Lines of improvement: Extend AWN coverage:
Manual
semi-automatic
Heuristic-based approach
GWC 2008 (Rodríguez et al, 2008a)
Bayesian Networks
LREC 2008 (Rodríguez et al, 2008b)
Improve the relation density.
Finding new relations
Manually revising relations existing for other languages (specially English)
using of roots as way of suggesting new relations
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32. AWN: What should we do Lines of improvement:
Building APIs for make easier the use of the database (Perl, Python, Prolog, C, Java, ...) including computing of semantic distances
Extending the coverage of NEs
Ex. from Wikipedia as Knowledge Source
Citala 2009 (AlKhalifa, Rodríguez, 2009)
Link AWN with other already available resources:
Wikipedia
CyC
Geonames
...
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33. Thank you for your attention
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