The document discusses Roberto Navigli giving a talk on BabelNet, a multilingual semantic network created by merging various knowledge resources, and Babelfy, a state-of-the-art multilingual word sense disambiguation system that leverages BabelNet. The talk outlines BabelNet and Babelfy, demonstrates Babelfy for word sense disambiguation, and discusses how working with BabelNet provides coverage of numerous resources by annotating with their combined knowledge.

1. Roberto Navigli
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
30th November 2017 – Rome, Italy
Machine Learning Meetup
http://lcl.uniroma1.it

2. 30/11/2017
Simone Ponzetto
Tiziano
Flati
Andrea
Moro
Daniele
Vannella
Francesco
Cecconi
30/11/2017BabelNet, Babelfy and Beyond!
Roberto Navigli
2
Alessandro
Raganato
Claudio
Delli Bovi
Taher Pilehvar

3. Outline of the talk
• Introduction to BabelNet
• Disambiguation and entity linking with Babelfy
• Demos:
– The Babelfy disambiguation system
– Multilingual concept and entity extraction
30/11/2017 3From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

4. 30/11/2017 4
A 5-year ERC Starting Grant (2011-2016)
on Multilingual Word Sense Disambiguation
http://multijedi.org
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

5. INTEGRATING
KNOWLEDGE
[Navigli & Ponzetto, ACL 2010
& Artificial Intelligence Journal 2012;
Pilehvar & Navigli, ACL 2014]
30/11/2017
2017 Artificial
Intelligence Prominent
Paper Award!
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

6. 30/11/2017BabelNet, Babelfy and Beyond!
Roberto Navigli
6
The resource diaspora

7. 30/11/2017 7
Key Objective 1: create knowledge for all languages
Multilingual Joint Word Sense Disambiguation
(MultiJEDI)
WordNet
MultiWordNet
WOLF
MCR
GermaNet
BalkaNet
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

8. It all started with merging WordNet and Wikipedia
[Navigli and Ponzetto, ACL 2010; AIJ 2012]
• A wide-coverage multilingual semantic network
including both encyclopedic (from Wikipedia) and
lexicographic (from WordNet) entries
Concepts from WordNetNamed Entities and specialized
concepts from Wikipedia
Concepts integrated from
both resources
30/11/2017 8From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

9. Creating a Multilingual Semantic Network
• Start from two large complementary resources:
– WordNet: full-fledged taxonomy
– Wikipedia: multilingual and continuously updated
{wheeled vehicle}
{self-propelled vehicle}
{motor vehicle} {tractor}
{car,auto, automobile,
machine, motorcar}
{convertible}
{air bag}
is-a
is-a
is-a
is-a
is-a
has-part
{golf cart,
golfcart}
is-a
{wagon,
waggon}
is-a
{accelerator,
accelerator pedal,
gas pedal, throttle}
has-part
{car window}
has-part
{locomotive, engine,
locomotive engine,
railway locomotive}
is-a
{brake}has-part
{wheel}
has-part
{splasher}
has-part
Get the best from both worlds
9From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

10. {wheeled vehicle}
{self-propelled vehicle}
{motor vehicle} {tractor}
{car,auto, automobile,
machine, motorcar}
{convertible}
{air bag}
is-a
is-a
is-a
is-a
is-a
has-part{golf cart,
golfcart}
is-a
{wagon,
waggon}
is-a
{accelerator,
accelerator pedal,
gas pedal, throttle}
has-part
{car window}
has-part
{locomotive, engine,
locomotive engine,
railway locomotive}
is-a
{brake}has-part
{wheel}
has-part
{splasher}
has-part
concepts
semantic relation
WordNet [Miller et al., 1990; Fellbaum, 1998]
10From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

11. 11
• Playing with senses
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
concepts
(unspecified) semantic relation
Wikipedia [The Web Community, 2001-today]
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

12. To merge or not to merge?
[Pilehvar and Navigli, ACL 2014]
• Measure the similarity of senses of the same word (but
from different resources)
• If they are similar enough, merge the corresponding two
concepts
WordNetplant#n#1plant#n#1
12From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

13. Structural Similarity with Personalized PageRank
[Pilehvar and Navigli, ACL 2014]
The resulting probabilities form a semantic signature

14. • We collect lexicalizations, definitions, translations,
images, etc. from each of the merged resources
Merging entries from different resources into BabelNet
14
WordNet
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

15. BabelNet: concepts and semantic relations (2)
• We encode knowledge as a labeled directed graph:
– Each vertex is a Babel synset (=synonym set)
– Each edge is a semantic relation between synsets:
• is-a (balloon is-a aircraft)
• part-of (gasbag part-of balloon)
• instance-of (Einstein instance-of physicist)
• …
• unspecified/relatedness (balloon related-to flight)
balloonEN, BallonDE,
aerostatoES, aerostatoIT,
pallone aerostaticoIT,
mongolfièreFR
1530/11/2017From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

16. Building BabelNet: Translating Babel synsets
1. Exploiting Wikipedia interlanguage links
pallone
aerostatico
globo
aerostàtico
Ballon
1630/11/2017From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

17. Building BabelNet: Translating Babel synsets
2. Filling the lexical translation gaps using a Machine
Translation system to translate the English lexicalizations of
a concept
• On August 27, 1783 in Paris, Franklin witnessed the
world's first hydrogen [[Balloon (aircraft)|balloon]]
flight.
• Le 27 Août, 1783 à Paris, Franklin vu le premier vol en
ballon d'hydrogène.
Statistical Machine Translation
1730/11/2017From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

18. What is BabelNet?
• A merger of resources of different kinds:
30/11/2017META Prize 2015: BabelNet
Roberto Navigli
18

19. 30/11/2017 19
• A merger of resources of different kinds:
– WordNet: the most popular computational lexicon of English
– Open Multilingual WordNet: a collection of open wordnets
– WoNeF: a French WordNet
– Wikipedia: the largest collaborative encyclopedia
– Wikidata: the largest collaborative knowledge base
– Wiktionary: the largest collaborative dictionary
– OmegaWiki: a medium-size collaborative multilingual dictionary
– GeoNames: a worldwide geographical database
– FrameNet lexical units
– VerbNet entries
– Microsoft Terminology: a computer science thesaurus
– High-quality automatic sense-based translations
What is BabelNet?
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

20. 30/11/2017 20
What is BabelNet?
• A merger of resources of different kinds:
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

21. Anatomy of BabelNet
30/11/2017Quando il linguaggio incontra l’informatica
Roberto Navigli
21
lemma lemma language
definitionpicture
further
languages
synonyms
synonyms
in other
languages

22. 30/11/2017 22
Anatomy of BabelNet
pronunciation
definition speech
definitions
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli
usage examples
generalizations and other relations

23. 30/11/2017 23
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

24. 30/11/2017 24
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

25. 30/11/2017 25
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

26. 30/11/2017 26
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
– 6M concepts and 7.7M named entities
– 119M word senses
– 378M semantic relations (27 relations per concept on avg.)
– 11M images associated with concepts
– 41M textual definitions
– 2M concepts with domains associated
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

27. 30/11/2017Multilingual Web Access – WWW 2015
Roberto Navigli
27
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
30/11/2017META Prize 2015: BabelNet
Roberto Navigli
27

28. 30/11/2017Multilingual Web Access – WWW 2015
Roberto Navigli
28
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
• "Dictionary of the future": semantic network structure
with labeled relations, pictures, multilingual synsets
30/11/2017META Prize 2015: BabelNet
Roberto Navigli
28

29. 30/11/2017 29
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
• "Dictionary of the future": semantic network structure
with labeled relations, pictures, multilingual synsets
• Full-fledged taxonomy: is-a relations are available for
both concepts and named entities (Wikipedia Bitaxonomy)
– Ferrari Testarossa is-a sports car
– BabelNet is-a semantic network & encyclopedic dictionary
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

30. 30/11/2017 30
Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
• "Dictionary of the future": semantic network structure
with labeled relations, pictures, multilingual synsets
• Full-fledged taxonomy: is-a relations are available for
both concepts and named entities (Wikipedia Bitaxonomy)
• Easy access: Java and HTTP RESTful APIs; SPARQL
endpoint (2 billion triples)
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

31. The core of the Linguistic
Linked Open Data cloud!

32. 30/11/2017 32
What can we do with BabelNet?
• Search and translate:
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

33. 30/11/2017META Prize 2015: BabelNet
Roberto Navigli
33
What can we do with BabelNet?

34. What can we do with BabelNet?
• Explore the network:
30/11/2017META Prize 2015: BabelNet
Roberto Navigli
34

35. BabelNet is now live!
• 284 languages
• 15 million concepts and named entities
• 1.8 billion semantic relations
30/11/2017Monolingual and multilingual, latent and
explicit representations of meaning
Roberto Navigli
35

36. Outline of the talk
• Introduction to BabelNet
• Disambiguation and entity linking with Babelfy
• Demos:
– The Babelfy disambiguation system
– Multilingual concept and entity extraction
30/11/2017 36From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

37. ADDRESSING
AMBIGUITY
[Moro, Raganato & Navigli,
TACL 2014]
37From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

38. Multilingual Joint Word Sense Disambiguation
(MultiJEDI)
Key Objective 2: use all languages to disambiguate one
3830/11/2017From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

39. So what?
3930/11/2017From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

40. Core step: Connect all candidate meanings
Thomas and Mario are strikers playing in Munich
4030/11/2017From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

41. Key points:
1. Disambiguate in any language with a multilingual
NLP pre-processing pipeline
2. Do not use training data
3. Use a knowledge-based algorithm that exploits a
processed version of the BabelNet graph
BabelNet: past, present and future
Roberto Navigli
Babelfy

42. Babelfy

43. Experimental Results:
Fine-grained (Multilingual) Disambiguation
Senseval-3
SemEval-2007
task 17
SemEval-2013 task 12
4330/11/2017BabelNet: past, present and future
Roberto Navigli

44. Babelfy demo
• Demo with a newspaper article
30/11/2017 44From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

45. Working with BabelNet saves you a
lot of time
• Annotating with BabelNet implies annotating with
WordNet, Wikipedia, OmegaWiki, Open Multilingual
WordNet, Wikidata, Wiktionary, ...
Key fact!
45
BabelNet
7
45From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

46. The Crazy Polyglot!
30/11/2017Multilingual Web Access – WWW 2015
Roberto Navigli
46

47. Live demo – Crazy polyglot!
KO 자연 언어 처리, 인공 지능
EN In todayʼs knowledge and information society
FR le paysage lexicographique est plus hétérogène que
jamais.
IT Possono le risorse stand-alone competere
ES con múltiples funciones, portale lexicográficas
multilingüe y servicios web,
ZH Web服务，定 制 的 喜 好 和 个 人 用 户 的 个 人 资 料 ？
30/11/2017 47From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

48. Same can be done with unstructured tags
30/11/2017 48From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

49. Same can be done with unstructured tags
30/11/2017BabelNet, the LLOD cloud and the Industry
Roberto Navigli
49

50. Neural Models for Word Sense Disambiguation
(Raganato, Delli Bovi, Navigli, EMNLP 2017)
• Sequence labeling:
30/11/2017 50
words & BabelNet
concepts
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

51. Neural Models for Word Sense Disambiguation
(Raganato, Delli Bovi, Navigli, EMNLP 2017)
• Training on English (SemCor sense annotated data)
• Testing on all English Senseval & SemEval test sets
30/11/2017 51From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

52. Neural Models for Word Sense Disambiguation
(Raganato, Delli Bovi, Navigli, EMNLP 2017)
• Training on English (SemCor sense annotated data)
• Testing on arbitrary languages (!) – SemEval 2013
– Using multilingual embeddings to encode words in the same space
30/11/2017 52From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

53. The future of BabelNet and related technologies
• The MultiJEDI ERC project is now over (but: the MOUSSE
ERC grant just started – in a couple of slides!)
• However, much work still to be done in this direction
• We created a Sapienza startup, Babelscape, with the key
objective of making BabelNet sustainable
• Income is reinvested in BabelNet and subsequent projects
Multilinguality for free, or why you should care about
linking vector representations to (BabelNet) synsets
Roberto Navigli

54. Demos
• Babelfy DONE
• Multilingual concept and term extraction
30/11/2017 55From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

55. Demo: multilingual concept and term extraction
• http://babeltex.org
– News in Italian: http://www.repubblica.it
– News in English: http://www.bbc.com/news
– News in Chinese: http://www.bbc.com/zhongwen/simp
30/11/2017 56From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

56. From MultiJEDI to MOUSSE: a Quantum Leap!
MultiJEDI (2011-16)
Large multilingual repository of
concepts: BabelNet
Disambiguation outputs a bag
of concepts
MOUSSE: Multilingual Open-text Unified Syntax-independent SEmantics
Roberto Navigli
57
MOUSSE (2017-22)
Huge repository of novel structured
universal semantic representations
We will construct a language-
independent structured semantic
representation of the sentence
1
2
1
2
CONCEPTS SENTENCE REPRESENTATIONS
DISAMBIGUATION SEMANTIC PARSING

57. Thanks or…
m i(grazie)
5830/11/2017
MultiJEDI (Starting Grant, 2011-2016) + MOUSSE (Consolidator Grant, 2017-2022)
From Text to Concepts and Back: Going
Multilingual with BabelNet in a Step or Two
Roberto Navigli

58. Roberto Navigli
Linguistic Computing Laboratory
http://lcl.uniroma1.it
@RNavigli