Onto.PT: Automatic construction of a Lexical Ontology for Portuguese

Transcrição

Onto.PT: Automatic construction of a Lexical Ontology
for Portuguese
Hugo Gonçalo Oliveira1 , Paulo Gomes
{hroliv,pgomes}@dei.uc.pt
Cognitive & Media Systems Group
CISUC, University of Coimbra
Lisbon, August 16, 2010
1
supported by FCT scholarship grant SFRH/BD/44955/2008
Gonçalo Oliveira & Gomes (CISUC)
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Outline
1
Introduction
Lexical ontologies
Goals
2
Approach
Information extraction from text
Synset discovery
Merging synset-based resources
Weighting triples
Assigning terms to synsets
Knowledge organisation
3
Current results
Relation extraction
Synset discovery
Wordnet establishment
4
Concluding remarks
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Introduction
Today’s applications
Need to understand information conveyed by natural language
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Introduction
Therefore, demand better access to knowledge on words and their
meanings!
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Introduction
Therefore, demand better access to knowledge on words and their
meanings!
Encoded in lexical ontologies
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Introduction
Lexical ontologies
Lexical ontologies
Such as Princeton WordNet [Fellbaum, 1998]
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Introduction
Lexical ontologies
Lexical ontologies
▶
Ontology + lexicon [Hirst, 2004]
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Introduction
Lexical ontologies
Lexical ontologies
▶
▶
Knowledge structured on words and their meanings
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Introduction
Lexical ontologies
Lexical ontologies
▶
▶
▶
▶
Cover the whole language
Not based on a specific domain
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Introduction
Lexical ontologies
Lexical ontologies
▶
▶
▶
▶
Cover the whole language
Not based on a specific domain
Typically handcrafted...
▶
Construction and maintenance involve time-consuming human effort!
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Introduction
Goals
Onto.PT
Automatic construction of a lexical ontology for Portuguese
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Introduction
Goals
Onto.PT
Extracted from different sources
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Introduction
Goals
Onto.PT
▶
▶
▶
Manually created thesauri
Language dictionaries/encyclopedias
Corpora
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Introduction
Goals
Onto.PT
▶
▶
▶
Corpora
Modelled after Princeton WordNet
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Introduction
Goals
Onto.PT
▶
▶
▶
Corpora
▶
▶
Synsets: groups of synonymous words
Synset-based relational triples
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Introduction
Goals
Onto.PT
▶
▶
▶
Corpora
▶
▶
Synsets: groups of synonymous words
Synset-based relational triples
WSD based on the knowledge already extracted, not on the context
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Approach
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Approach
Examples
From dictionaries:
▶
tenreiro, n -- terneiro, novilho ou bezerro.
→ terneiro SYNONYM OF tenreiro
→ novilho SYNONYM OF tenreiro
→ bezerro SYNONYM OF tenreiro
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Approach
Examples
From dictionaries:
▶
▶
ébola, n -- virose que provoca febres e hemorragias
→ ébola CAUSATION OF febres
→ ébola CAUSATION OF hemorragias
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Approach
Examples
From dictionaries:
▶
▶
ébola, n -- virose que provoca febres e hemorragias
→ ébola CAUSATION OF febres
→ ébola CAUSATION OF hemorragias
From textual corpora:
▶
O automobilismo (também conhecido como corridas de
automóveis ou desporto motorizado) é um desporto...
→ automobilismo SYNONYM OF corridas de automóveis
→ automobilismo SYNONYM OF desporto motorizado
→ desporto HYPERNYM OF automobilismo
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Approach
Synset discovery
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Approach
Synset discovery
Synonymy lexical network – example
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Approach
Synset discovery
Synonymy networks tend to have a clustered structure
Goal: Identify synsets taking advantage of clusters
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Approach
Synset discovery
Approach: Clustering algorithm over the synonymy lexical network
(see poster [Oliveira and Gomes, 2010])
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Approach
Synset discovery
Approach: Clustering algorithm over the synonymy lexical network
(see poster [Oliveira and Gomes, 2010])
Keep ambiguity: clusters might be overlapping!
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Approach
Synset discovery
Clustering – example
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Approach
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Approach
Merging synsets from different thesauri
For each synset Ti ∈ T , select Bj ∈ B with higher c = Ti ∩ Bj /Ti ∪ Bj 2
B1 = (diva, beldade, beleza, deidade, deusa, divindade)
B2 = (divindade, deidade, deus, nume)
2
Jaccard coefficient
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Approach
T1 = (divindade, diva, deusa)
2
Jaccard coefficient
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Approach
▶
▶
2
c(T1 , B1 ) =
c(T1 , B2 ) =
1
3
1
6
Jaccard coefficient
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Approach
▶
▶
c(T1 , B1 ) =
c(T1 , B2 ) =
1
3
1
6
N = B1 ∪ T1 = (diva, beldade, beleza, deidade, deusa, divindade)
2
Jaccard coefficient
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Approach
Weighting triples
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Approach
Weighting triples
Weighting triples
Frequency of extraction
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Approach
Weighting triples
Weighting triples
Corpus distributional similarity metrics (e.g. LSA
[Deerwester et al., 1990], PMI [Turney, 2001] )
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Approach
Weighting triples
Weighting triples
Web distributional similarity metrics (e.g. WebJaccard, WebOverlap
[Bollegala et al., 2007])
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Approach
Weighting triples
Weighting triples
Web distributional similarity metrics (e.g. WebJaccard, WebOverlap
[Bollegala et al., 2007])
Define filters based on weights
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Approach
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Approach
Mapping methods
Input:
▶
▶
Thesaurus T , containing synsets
Term-based semantic network, N, where each edge has a type R
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Approach
Mapping methods
Input:
▶
▶
Goal: map a R b ∈ N to A R B, (A, B) ∈ T
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Approach
Mapping methods
Input:
▶
▶
Goal: map a R b ∈ N to A R B, (A, B) ∈ T
Output: semantic network W , whose nodes are synsets, which relate
to other synsets by means of semantic relations (wordnet)
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Approach
Mapping procedures
Baseline
▶
A and B are random synsets containing a and b respectivelly
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Approach
Mapping procedures
Baseline
▶
Related proportion [Gonçalo Oliveira and Gomes, 2010]
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Approach
Mapping procedures
Baseline
▶
Related proportion [Gonçalo Oliveira and Gomes, 2010]
Cosine similarity
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Approach
Related proportion
Assignment of a (in a R b) to A:
1
Fix b
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Approach
Related proportion
1
Fix b
2
Sa ⊂ T : Sai ∈ Sa , a ∈ Sai
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Approach
Related proportion
1
Fix b
2
▶
a is not in T ? create synset A = (a), a → A
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Approach
Related proportion
1
Fix b
2
▶
3
For each Sai ∈ Sa ,
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Approach
Related proportion
1
Fix b
2
▶
3
▶
pai =
nai
∣Sai ∣ ,
nai = number of terms tj ∈ Sai : (tj R b)
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Approach
Related proportion
1
Fix b
2
▶
3
▶
pai =
★
★
★
nai
∣Sai ∣ ,
Sa1 = (a, c, d, e), pa1 = 34
Sa2 = (a, f, g ), pa2 = 23
Sa3 = (a, h, i, j), pa3 = 41
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Approach
Related proportion
1
Fix b
2
▶
3
▶
pai =
★
★
★
▶
nai
∣Sai ∣ ,
Sa1 = (a, c, d, e), pa1 = 34
Sa2 = (a, f, g ), pa2 = 23
Sa3 = (a, h, i, j), pa3 = 41
a → Sa1
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Approach
Cosine similarity
1
M = term-term matrix based on the adjacencies of the lexical network
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Approach
Cosine similarity
1
2
Collect all the synsets with a, Sa ⊂ T , and all synsets with b, Sb ⊂ T
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Approach
Cosine similarity
1
2
3
For each A ∈ Sa and B ∈ Sb , with terms Ai ∈ A and Bj ∈ B:
∣A∣ ∑
∣B∣
∑
sim(A, B) =
cos(Ai , Bj )
i=1 j=1
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Approach
Cosine similarity
1
2
3
For each A ∈ Sa and B ∈ Sb , with terms Ai ∈ A and Bj ∈ B:
∣A∣ ∑
∣B∣
∑
sim(A, B) =
4
cos(Ai , Bj )
i=1 j=1
∣A∣∣B∣
Select the pair of synsets with the highest similarity
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Approach
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Approach
Transitivity
▶
if R is transitive (e.g. SYNONYMY, HYPERNYMY, ...):
(A R B) ∧ (B R C ) → (A R C )
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Approach
Transitivity
▶
if R is transitive (e.g. SYNONYMY, HYPERNYMY, ...):
(A R B) ∧ (B R C ) → (A R C )
Inheritance
▶
if R is not a HYPERNYMY or HYPONYMY relation:
(A HYPERNYM OF B) ∧ (A R C ) → (B R C )
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Current results
Relation extraction
Triples extracted from dictionaries
Dicionário da Lı́ngua Portguesa (PAPEL 2.0)
Dicionário Aberto (DA)
Relation
Synonymy
Hypernymy
Part-of
Member-of
Causation
Purpose
Arguments
noun,noun
verb,verb
adj,adj
adv,adv
noun,noun
noun,noun
noun,adj
noun,noun
adj,noun
noun,noun
adj,noun
verb,noun
noun,noun
verb,noun
verb,adj
PAPEL 2.0
37,452
21,465
19,073
1,171
62,591
2,805
3.721
5.929
883
1.013
498
6,399
2,886
5,192
260
DA
20,910
8,715
7,353
605
59,887
1,795
4,902
1,564
59
264
166
5,714
1,760
3,383
186
Examples
auxı́lio, contributo
tributar, colectar
flexı́vel, moldável
após, seguidamente
planta, salva
cauda, cometa
tampa, coberto
ervilha, Leguminosas
celular, célula
fricção, assadura
reactivo, reacção
limpar, purgação
defesa, armadura
fazer rir, comédia
corrigir, correccional
Table: Examples of triples
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Current results
Relation extraction
Relations extracted from Wikipedia abstracts
Relation
Synonymy
Hypernymy
Part-of
Causation
Purpose
Quantity
11,862
29,563
1,287
520
743
Example
estupro,violação
estilo de música,folk
jejuno,intestino
parasita,doença
construção,terracota
Sample
286
322
268
244
264
Correct
86,1%
59,1% 3
52,6%
49,6%
57,0%
Agreement
91,2%
93,1%
78,4%
79,5%
82,2%
Table: Examples and validation of relations
3
In 30%, grammars/tagger could not identify complete scientific names as in:
O Iriatherina werneri é uma espécie de peixe de aquário
→ peixe de aquário HYPERNYM OF werneri
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Current results
Synset discovery
Thesaurus
TeP4 thesaurus
OpenThesaurus.PT (OT)5
Clustered PAPEL (CLIP)
TeP merged with OT, merged with CLIP (TOP)
Words
Synsets
Quantity
Ambiguous
Most ambiguous
Quantity
Avg. size
Biggest
TeP
17,158
5,867
20
8,254
3.51
21
OT
5,819
442
4
1,872
3.37
14
CLIP
23,741
12,196
47
7,468
12.57
103
TOP
30,554
13,294
21
9,960
6.6
277
Table: (Noun) thesauruses in numbers.
4
5
http://www.nilc.icmc.usp.br/tep2/index.htm
http://openthesaurus.caixamagica.pt/
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Current results
Synset discovery
Manual validation
CLIP
CLIP’
TOP
TOP’
Sample
519 sets
310 sets
480 sets
448 sets
Correct
65.8%
81.1%
83.2%
86.8%
Incorrect
31.7%
16.9%
15.8%
12.3%
N/A
2.5%
2.0%
1.0%
0.9%
Agreement
76.1%
84.2%
82.3%
83.0%
Table: Results of manual synset validation.
CLIP’ and TOP’ only consider synsets with 10 or less words.
▶
The quality is higher for smaller synsets.
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Current results
Resulting WordNet – related proportion
Term-based triples
Mapped
1st
Same synset
Already present
Semi-mapped triples
Mapped
2nd Could be inferred
Already present
Synset-based triples
Hypernym of
62,591
27,750
233
3,970
7,952
88
50
13
23,572
Part of
2,805
1,460
5
40
262
1
0
0
1,416
Member of
5,929
3,962
12
167
357
0
0
0
3,783
Table: Results of triples mapping
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Current results
Automatic validation
For each triple, A R B
1 Compile a set of textual patterns denoting R, e.g.:
▶
▶
(hypo) é um∣uma (tipo∣forma∣variedade∣...)* de (hyper)
(whole/group) é um (grupo∣conjunto∣...) de (part/member)
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Current results
▶
▶
2
Score the triple with the help of Google:
∣A∣ ∑
∣B∣
∑
score =
found(Ai , Bj , R)
i=1 j=1
∣A∣ ∗ ∣B∣
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Current results
▶
▶
2
Score the triple with the help of Google:
∣A∣ ∑
∣B∣
∑
score =
Relation
Hypernymy of
Member of
Part of
found(Ai , Bj , R)
i=1 j=1
∣A∣ ∗ ∣B∣
Sample size
419 synsets
379 synsets
290 synsets
Validation
44,1%
24,3%
24,8%
Table: Automatic validation of triples
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Concluding remarks
Concluding remarks
Answer to the growing demand on semantically aware applications
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Concluding remarks
Concluding remarks
Lack of public domain lexico-semantic resources for Portuguese
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Concluding remarks
Concluding remarks
Export resources to different data formats
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Concluding remarks
Concluding remarks
WSD without a context:
▶
Clustering for establishing synsets
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Concluding remarks
Concluding remarks
▶
▶
Disambiguation of terms based on the extracted knowledge
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Concluding remarks
Concluding remarks
▶
▶
▶
Further organisation
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Concluding remarks
Concluding remarks
▶
▶
▶
Further organisation
Check http://ontopt.dei.uc.pt for updates and available
resources
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The end
References
Bollegala, D., Matsuo, Y., and Ishizuka, M. (2007).
Measuring semantic similarity between words using web search engines.
In Proc. 16th International conference on World Wide Web (WWW’07), pages 757–766, New York, NY, USA. ACM.
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990).
Indexing by latent semantic analysis.
Journal of the American Society for Information Science, 41:391–407.
Fellbaum, C., editor (1998).
WordNet: An Electronic Lexical Database (Language, Speech, and Communication).
The MIT Press.
Gonçalo Oliveira, H. and Gomes, P. (2010).
Towards the automatic creation of a wordnet from a term-based lexical network.
In Proceedings of the ACL Workshop TextGraphs-5: Graph-based Methods for Natural Language Processing.
Hirst, G. (2004).
Ontology and the lexicon.
In Staab, S. and Studer, R., editors, Handbook on Ontologies, International Handbooks on Information Systems, pages
209–230. Springer.
Oliveira, H. G. and Gomes, P. (2010).
Automatic creation of a conceptual base for portuguese using clustering techniques.
In Proc. 19th European Conference on Artifical Intelligence (ECAI 2010).
Turney, P. D. (2001).
Mining the web for synonyms: PMI–IR versus LSA on TOEFL.
In Proc. 12th European Conference on Machine Learning (ECML-2001), volume 2167, pages 491–502. Springer.
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The end
Thank you!
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Onto.PT: Automatic construction of a Lexical Ontology for Portuguese

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