1 gloss-based semantic similarity metrics for predominant sense acquisition ryu iida nara institute...
DESCRIPTION
IJCNLP2008 Jan 10, Focus How to calculate the semantic similarity score without semantic relations such as hyponym Explore the potential use of the word definitions (glosses) instead of WordNet- style resources for porting McCarthy et al.’s method to other languagesTRANSCRIPT
1
Gloss-based Semantic Similarity Metrics for
Predominant Sense Acquisition
Ryu Iida Nara Institute of Science and TechnologyDiana McCarthy and Rob Koeling University of Sussex
IJCNLP2008 Jan 10, 2008
2
IJCNLP2008 Jan 10, 2008 Word Sense Disambiguation Predominant sense acquisition
Exploited as a powerful back-off strategy for word sense disambiguation
McCarthy et al (2004): Achieved 64% precision on Senseval2 all-
words taskStrongly relies on linguistic resources such
as WordNet for calculating the semantic similarity
Difficulty: porting it to other languages
3
IJCNLP2008 Jan 10, 2008 Focus How to calculate the semantic
similarity score without semantic relations such as hyponym
Explore the potential use of the word definitions (glosses) instead of WordNet-style resources for porting McCarthy et al.’s method to other languages
4
IJCNLP2008 Jan 10, 2008 Table of contents
1. Task2. Related work: McCarthy et al (2004) 3. Gloss-based semantic similarity
metrics4. Experiments
WSD on the two datasets: EDR and Japanese Senseval2 task
5. Conclusion and future directions
5
IJCNLP2008 Jan 10, 2008 Word Sense Disambiguation (WSD) task
select the correct sense of the word appearing in the context
I ate fried chicken last Sunday.
sense id gloss1 a common farm bird that is kept for its meat and eggs2 the meat from this bird eaten as food3 informal someone who is not at all brave4 a game in which children must do something
dangerous to show that they are braveSupervised approaches have been
mainly applied to learn the context
6
IJCNLP2008 Jan 10, 2008 Word Sense Disambiguation (WSD) task (Cont’d)Estimate the most predominant sense
of a word regardless of its contextEnglish coarse-grained all words task
(2007)Choosing most frequent senses: 78.9%Best performing system: 82.5%
Systems using a first sense heuristic have relied on sense-tagged dataHowever, sense-tagged data is expensive
7
IJCNLP2008 Jan 10, 2008 McCarthy et al. (2004)’s unsupervised approach Extract top N neighbour words of the target word acc
ording to the distributional similarity score (simds)
Calculate the prevalent score of each senseCalculate simds weighted by the semantic similarity score
(simss)Sum up all the weighted simds of top N neighboursSemantic similarity: estimated from linguistic resources
(e.g. WordNet)
Output the sense which has the maximum prevalent score
8
IJCNLP2008 Jan 10, 2008 McCarthy et al. (2004)’s approach: An example
neighbour simds
turkey 0.1805meat 0.1781... ...tomato 0.1573
sense2: the meat from this bird eaten as food.sense3: informal someone who is not at all brave.
chicken
prevalence(sense2) = 0.0271 + 0.0365 + ... + 0.0157 = 0.152
distributional similarity score
0.20
0.10...
0.15simss(word, sense2)
0.0365
0.0157...
0.0271weighted simds
=
semantic similarity score (from WordNet)
9
IJCNLP2008 Jan 10, 2008 McCarthy et al. (2004)’s approach: An example
neighbour simds
turkey 0.1805meat 0.1781... ...tomato 0.1573
simss(word, sense3)
0.010.02...0.01
weighted simds
0.00180.0037...0.0016
=
sense2: the meat from this bird eaten as food.sense3: informal someone who is not at all brave.
chicken
prevalence(sense3) = 0.0018 + 0.0037 + ... + 0.0016 = 0.023
prevalence(sense2) = 0.152
prevalence(sense2) > prevalence(sense3) predominant sense: sense2
10
IJCNLP2008 Jan 10, 2008 ProblemWhile the McCarthy et al.’s method works wel
l for English, other inventories do no always have WordNet-style resources to tie the nearest neighbors to the sense inventory
While traditional dictionaries do not organise senses into synsets, they do typically have sense definitions (glosses) associated with the senses
11
IJCNLP2008 Jan 10, 2008 Gloss-based similarityCalculate similarity between two glosses in a
dictionary as semantic similarity
simlesk: simply calculate the overlap of the content words in the glosses of the two word senses
simDSlesk: use distributional similarity as an approximation of semantic distance between the words in the two glosses
12
IJCNLP2008 Jan 10, 2008 lesk: Example
simlesk(chicken, turkey) = 2“meat” and “food” are overlapped in two glosses
word glosschicken
the meat from this bird eaten as food
turkey the meat from a turkey eaten as food
13
IJCNLP2008 Jan 10, 2008 lesk: Example
simlesk(chicken, tomato) = 0No overlap in two glosses
word glosschicken
the meat from this bird eaten as food
tomato a round soft red fruit eaten raw or cooked as a vegetable
14
IJCNLP2008 Jan 10, 2008
simDSlesk (chicken, tomato)= 1/3 (0.1843 + 0.1001 + 0.1857) = 0.1557
DSlesk Calculate distributional similarity scores of
any pairs of nouns in two glosses
Output the average of the maximum distributional similarity of all the nouns in target word
simds(meat, fruit) = 0.1625, simds(meat, vegetable) = 0.1843,
simds(bird, fruit) = 0.1001, simds(bird, vegetable) = 0.0717,
simds(food, fruit) = 0.1857, simds(food, vegetable) = 0.1772
15
IJCNLP2008 Jan 10, 2008 DSlesk
),(max),()( jinWSwsiDSlesk wswssimnwssim
j
),(max ji ggsim
)(ba )( ji gg: noun appearing in
ig iws: gloss of word sense
),(max||
1),( basimga
ggsim dsga gb
iji
ij
16
IJCNLP2008 Jan 10, 2008 Apply Gloss-based similarity to McCarthy et al.’s approach
neighbour simds
turkey 0.1805meat 0.1781... ...tomato 0.1573
simDSlesk(word, sense2)
0.34530.2323...0.1557
weighted simds
0.06230.0414...0.0245
=
sense2: the meat from this bird eaten as food.sense3: informal someone who is not at all brave.
chicken
prevalence(sense2) = 0.0623 + 0.0414 + ... + 0.0245 = 0.2387
17
IJCNLP2008 Jan 10, 2008 Table of contents1. Task
2. Related work: McCarthy et al (2004)
3. Gloss-based semantic similarity metrics
4. Experiments WSD on the two datasets: EDR and
Japanese Senseval2 task5. Conclusion and future directions
18
IJCNLP2008 Jan 10, 2008 Experiment 1: EDRDataset: EDR corpus
3,836 polysemous nouns (183,502 instances)Adopt the similarity score proposed by Lin (1
998) as the distributional similarity score9-years Mainichi newspaper articles and 10-years
Nikkei newspaper articlesJapanese dependency parser CaboCha (Kudo and
Matsumoto, 2002)Use 50 nearest neighbors in line with McCart
hy et al. (2004)
19
IJCNLP2008 Jan 10, 2008 MethodsBaseline
Select one word sense at random for each word token and average the precision over 100 trials
Unsupervised: McCarthy et al. (2004)Semantic similarity:
Jiang and Conrath (1997) (jcn), lesk, DSlesk
Supervised (Majority)Use hand-labeled training data for obtaining the p
redominant sense of the test words
20
IJCNLP2008 Jan 10, 2008 Results: EDR
DSlesk is comparable to jcn without the requirement for semantic relations such as hyponymy
recall precisionbaseline 0.402 0.402jcn 0.495 0.495lesk 0.474 0.488DSlesk 0.495 0.495upper-bound 0.745 0.745supervised 0.731 0.731
21
IJCNLP2008 Jan 10, 2008 Results: EDR (Cont’d)
All methods for finding a predominant sense outperform the supervised one for item with little data (≤ 5), indicating that these methods robustly work even for low frequency data where hand-tagged data is unreliable
all freq ≤ 10 freq ≤ 5baseline 0.402 0.405 0.402jcn 0.495 0.445 0.431lesk 0.474 0.448 0.426DSlesk 0.495 0.453 0.433upper-bound 0.745 0.674 0.639supervised 0.731 0.519 0.367
22
IJCNLP2008 Jan 10, 2008 Experiment 2 and Results: Senseval2 in Japanese50 nouns (5,000 instances)4 methods
lesk, DSlesk, baseline, supervised
fine-grained coarse-grainedbaseline 0.282 0.399lesk 0.344 0.501DSlesk 0.386 0.593upper-bound 0.747 0.834supervised 0.742 0.842
precision = recall
sense-id: 105-0-0-2-0 fine-grainedcoarse-grained
23
IJCNLP2008 Jan 10, 2008 ConclusionWe examined different measures of semantic
similarity for finding a first sense heuristic for WSD automatically in Japanese
We defined a new gloss-based similarity (DSlesk) and evaluated the performance on two Japanese WSD datasets (EDR and Senseval2), outperforming lesk and achieving a performance comparable to the jcn method which relies on hyponym links which are not always available
24
IJCNLP2008 Jan 10, 2008 Future directionsExplore other information in the glosses, suc
h as words of other POS and predicate-argument relations
Group fine-grained word senses into clusters, making the task suitable for NLP applications (Ide and Wilks, 2006)
Use the results of predominant sense acquisition as a prior knowledge of other approachesGraph-based approaches (Mihalcea 2005, Nasta
se 2008)