brighter than goldvene.ro/figurative-comparisons/brighter-slides.pdfcomparisons assert likeness of...
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Brighter than Gold: Figurative Language in User Generated ComparisonsVlad Niculae MPI-SWS Cornell University
Cristian Danescu-Niculescu-Mizil MPI-SWS Cornell University
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ComparisonsAssert likeness of two things;
Point out shared properties.
Aren’t always meant to be taken literally!
“Sterling is much cheaper than gold.”
“Her voice makes the song shine brighter than gold.”
Figurative comparison(simile) ●
Literal comparison ●
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This talk: What makes comparisons figurative?
“Sterling is much cheaper than gold.”
“Her voice makes the song shine brighter than gold.”
Figurative comparison(simile) ●
Literal comparison ●
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Most research is on metaphor.
- Conceptual interpretation (Shutova et al., 2013)
- Identification tree kernels (Hovy et al., 2013) conceptual features (Tsvetkov et al., 2014)
Less (computational) research on simile.
- Irony, humor (Veale et al., 2012)
Figurative language Metaphor, simile, etc.
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But can we tell if a comparison is literal?
•Describe new things in terms of given things.“A unicorn is like a horse, but it has a horn.”
•Rank with respect to a property.“The cord is more durable than the adapter.”
Applications in Knowledge Extraction(Tandon et al., 2014, Lofi et al., 2014, inter alia)Assumption: comparisons are literal.
Role of comparisons
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For figurative language, context is important!
social, topical…
our main insight:
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applying our insight:
Does topic information differentiate literal and figurative comparisons?
Can we take advantage of the structure of the comparison?
How does social context drive figurative language use?
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Rich in comparisons need to describe products
Rich in social context helpfulness, star ratings
Topical context comes for free product categories
Data we analyze:Amazon.com product reviews
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1400 sentences with marked comparisons.
Figurative scores from three Amazon Mechanical Turk annotators.
Freely available: http://vene.ro/figurative-comparisons/
Figurative Comparison Dataset
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Characterizing figurative comparisons
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% fi
gura
tive
0
10
20
30
40
Books Music Jewelry Electronics
Topical context Is figurativeness more likely
in certain product categories?
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melody
melody
(music)
(books)
The song feels like a boring
Her book is like a sweet
specificity(word, topic) =#(word appears in topic)
#(word appears in all topics)
96%
3%
Topical context Is figurative word choice
topic-specific?
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melody
melody
(music)
(books)
song :
book :
specificity(word, topic) =#(word appears in topic)
#(word appears in all topics)
96%
3%
Topical context Is figurative word choice
topic-specific?
0.4
0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
spec
ifici
ty
fig. lit.
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The song shines brighter than gold.Sterling is cheaper than gold.
≉
≈
Comparison structure Similarity between
the things being compared.
*cosine similarity in a vector space model
0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
sim
ilarit
y
fig. lit.
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• Concreteness kiss vs. happiness
• Imageability cinnamon vs. casque
• Supersenses: coarse categories annotate: {communication, creation}
• Vector space embeddings
Metaphor features Inspired by Tsvetkov et al. (2014)
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Are these insights predictive?
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Linguistic insight features:
Topic-aware
• Review category
• Word specificity
Topic-agnostic
• Structural similarity
• Metaphor features (concreteness, imageability, supersenses, embeddings)
• Definiteness
Classification (logistic regression)
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Why so strong?
• “the hero is truly larger than life”
• “the headphonessounded like crap”
method acc. F
bag-of-words 80% 75%
Why so strong?
“the hero is truly larger than life”
“the headphonessounded like crap”
Classification (logistic regression)
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Top features:
1. Structural similarity “the older man was wiser than the boy”
2. Topic specificity“the cord’s more durable than the adapter”
3. Imageability “the explanationsare as clear as mud”
method acc. F
bag-of-words 80% 75%
linguistic insight 86% 79%
Classification (logistic regression)
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!
The two approaches are complementary.
method acc. F
bag-of-words 80% 75%
linguistic insight 86% 79%
both 88% 82%
(p < 0.05)
Classification (logistic regression)
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!
Optimistic estimation:
Majority voting among three Mechanical Turk workers
method acc. F
bag-of-words 80% 75%
linguistic insight 86% 79%
both 88% 82%
human 96% 94%
Classification (logistic regression)
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20 40 60 80 100
Percentage of training data used
0.65
0.70
0.75
0.80
0.85
Acc
ura
cy
all linguistic insights
metaphor inspired
(p < 0.05)
Why not just use a metaphor system?
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Practical importance
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Figurative languageand social context
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Social context of reviews
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(p < 0.05)1 2 3 4 5
0.30
0.32
0.34
0.36
0.38
star ratingratio
of f
igur
ativ
e co
mpa
rison
s Intrinsic opinion
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(p < 0.05)0.0 0.2 0.4 0.6 0.8 1.0
0.30
0.35
0.40
0.45
0.50
ratio
of f
igur
ativ
e co
mpa
rison
s
helpfulness
Extrinsic opinion (how the review is perceived)
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“He uses statistics as a drunken man uses lamp-posts… for support rather
than illumination.”
ConclusionsCompiled a dataset of figurative & literal comparisons.
Figurative language is tied to a social context.
Topic information is relevant for figurativeness.
Similes benefit from specific computational treatment.
― Andrew Lang
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― Terry Pratchett, Hogfather
“Getting an education was a bit like a communicable sexual disease. It made
you unsuitable for a lot of jobs and then you had the urge to pass it on.”
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Extra slides
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method # feat acc. P R F AUC
bag-of-words 1840 .80 .64 .90 .75 .89
metaphor-inspired 345 .75 .60 .72 .65 .84
domain-specific 8 .69 .51 .81 .63 .76
all insight features 365 .86 .76 .83 .79 .92
all of the above 2202 .88 .80 .84 .82 .94
human .96 .92 .96 .94
Training set size = test set size(408 comparisons, 66% literal)
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Matching comparisonsThe [song Topic] [shines Event] [brighter Property][than Comparator] [gold Vehicle]
3 types of patterns:
• T is like V
• T is as P as V
• T is P-er than V
E */VB
C like/IN
P T
V
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Data collectionAmazon.com
reviews
-----
Validate(3x MTurk)
---
Annotate 1-4(3x MTurk)
-(3, 4, 4)-(4, 2, 1)-(1, 2, 1)
extractcomparisons consensus,
binarize
--
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Concreteness and imageability(Tsvetkov et al., 2014 for metaphor)
more imageable less imageable
more concretecinnamon,
kisscasque,pugilist
less concretedevil,
happinessaspect,
however
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(Tsvetkov et al., 2014 for metaphor)
Concreteness, imageability: MRC Psycholinguistic Database
Supersenses: WordNet for nouns, verbs Tsvetkov et al. (2014) for adjectives
Logistic regression on word embeddings.
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Metaphors that can’t be rephrased as similes:
• “Her argument was somewhat murky.”
• “The house had great bones.”
Similes that can’t be rephrased as metaphors:
• “The windshield wipers made a great clatter like two idiots clapping in church.”
• “Two bears sat facing each other like two matrons having tea.”
Metaphor vs. Simile Israel et al (2004)