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![Page 1: Gaiku Generating Haiku with Word Associations Norms Yael Netzer, David Gabay, Yoav Goldberg and Michael Elhadad Department of Computer Science Ben Gurion.](https://reader036.fdocuments.us/reader036/viewer/2022062407/56649d375503460f94a0ff8a/html5/thumbnails/1.jpg)
Gaiku Generating Haiku withWord Associations Norms
Yael Netzer, David Gabay , Yoav Goldberg and Michael ElhadadDepartment of Computer ScienceBen Gurion University of the Negev Israel
CALC’09 May 35th 2009
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Creativity“the forming of associative elements into new combinations which either meet specified requirements or are in some way useful…”
[Mendick 1969]
Three main pathes to a creative solution:- serendipity- similarity- mediation
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Poetry
ComputationalCreativity
WAN
Generating Haiku!
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Haiku
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Haiku• Form of poetry originated in Japan, 16th
Century• Three lines of 5,7,5 phonetic units (mora) • Use present tense and use no judgmental
words• Adopted in Western languages, 20th
Century• 5,7,5 3 short lines• Traditionaly, reference to nature and
seasons, but modern Haiku are not restricted
Basho Haiku
古池や蛙飛込む水の音old pond . . .a frog leaps inwater’s sound
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iced over pond
I skip a rock
the entire width
blossomlessbut not unloved the old magnolia
fishing guidesboat in the backgrounda new trip
a holy cowa carton of milkseeking a church
blind snakeson the wet grasstombstoned terror
first date —the little pileof anchovies
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Poetry Generation
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Bo
y
S ul
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Bo
y
S ul
Structure
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Bo
y
S ul
Structure
Content
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Bo
y
S ul
3 lines,Grammatical,Haiku-like
Inspiring,Interesting,Intriguing,Joyful,…
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Previous works
- Manurung [2003]- Manurung et al. [2000]- Gervas [2001]
Emphasize on Structure,Less on Content
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Body / Structure
• Haiku Corpus– ~3,500 Haiku in English– Various sources
• amateurish sites• children’s writings• translations of classic Japanese Haiku of Bashu
and others• ’official’ sites of Haiku Associations (e.g.,
Haiku Path - Haiku Society of America).
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Body / Structure
POS TagCount
Line 1 Patterns:
280 JJ NN276 NN NN ...
Line 2 Patterns:
64 DT_the JJ NN
…
Line 3 Patterns:
.…Count
Pattern Transitions:
P(line2==DT_the NN |
line1==JJ NN) ... =
…
NN IN_of NNPDT_a NN IN_ofNNS
NN NNNNS CC NNSIN_on DT_a NN NN
…
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Body / StructureLine 1 Patterns:280 JJ NN276 NN NN...
Line 2 Patterns:64 DT_the JJ NN …
Line 3 Patterns:
….
Pattern Transitions:P(line2==DT_the NN | line1==JJ NN) = ... …
Google 1T-Web / Proj Gutenberg
POS Tagged
mat
ch
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Body / StructureGoogle 1T-Web / Proj Gutenberg
POS Tagged
mat
ch
JJ NNSDT_a JJ NNIN_of NN
Line 1 Patterns:280 JJ NN276 NN NN...
Line 2 Patterns:64 DT_the JJ NN …
Line 3 Patterns:
….
Pattern Transitions:P(line2==DT_the NN | line1==JJ NN) = ... …
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Body / StructureGoogle 1T-Web / Proj Gutenberg
POS Tagged
mat
ch
JJ NNSDT_a JJ NNIN_of NN
pouring catsa pilot careof fighter
Line 1 Patterns:280 JJ NN276 NN NN...
Line 2 Patterns:64 DT_the JJ NN …
Line 3 Patterns:
….
Pattern Transitions:P(line2==DT_the NN | line1==JJ NN) = ... …
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Body / StructureLine 1 Patterns:
AA BB CC / 12
BB CC DD / 10
…
Line 2 Patterns:
CC DD EE / 20
…
Line 3 Patterns:
.…
Pattern Transitions:
P(Line2=AA BB | Line1= XX YY)
…
Google 1T-Web / Proj Gutenberg
POS Tagged
mat
ch
Grammatical outputPreserves Haiku
“Texture”
JJ NNSDT_a JJ NNIN_of NN
pouring catsa pilot careof fighter
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Soul?
• Requirements: good “story”– cohesive– surprising– provoke feelings/emotions– metaphorical– “Should leave the reader wondering…”
… Creative!
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Soul?• An idea:
capture “story” seed as sequence of concepts
butterfly, spring, flowerthief , steal , jailmosquito, blood, vampire
but not any seed will docat , feline , claw too
cohesivecomputer , coat , queen too
divergent
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Soul?
Is WordNet a good soul?
not really
it may give cohesiveness,but bad stories
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Soul?
Is WordNet a good soul?
not really
We actually measured it
in Haiku Corpus
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Butterfly Spring Flower
• The connection between these words is reconstructable by human
• It is not available in WordNet
• Where can we find such relations?
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Word Association Norms
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Word Association Norms (WAN)
• Collection of cue words a set of free associations (targets) with quantitative and statistical measures.
(mouse CAT 0.5, RAT 0.08, CHEESE 0.07, HOLE 0.05…)
• Given a cue - collect immediate responses of first word that comes to mind.
• Largest WAN we know for English is the University of South Florida Free Association Norms (Nelson et al., 1998).http://w3.usf.edu/FreeAssociation/
• 5,019 cue words and 10,469 additional target that were collected with more than 6,000 participants since 1973.
WAN – weighted directed graph, nodes are stemmed words.
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water
flowerbutterfly
spring
fall fall
green
water
bloom
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Why Word Associations• Added value of WAN: an insight on language,
not found in WordNet or are hard to acquire from corpora [Sinopalnikova & Smrz 2004]
• Associative thinking takes part in the process of writing and reading poetry.
• Haiku, because so short - relies on lexical associations for concept progression
Hypothesis: word-associations are good catalyzers for creativity, can be used as a building block in the creative process of Haiku generation.
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We first test this hypothesis by analyzing a corpus of existing Haiku poems.
• Can the creativity of text as reflected in word associations be quantified?
• Are Haiku poems indeed more associative than newswire text or prose?
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Two nodes are connected iff one of them is a cue for the other.
Associative distance:number of edges in the shortest path between the
words in the associations-graph.
WordNet distance:number of edges in the shortest path between any
synset of one word to any synset of the other word
Associativity of a text - the number of associated word pairs in the text, normalized by the number of word pairs in the text of which both words are in the WAN.
WordNet-relations level - the number of WordNet-related word pairs in the text.
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Average AssociativityWe measure the associavity and WordNet relations levels of 200 of the Haiku in our Haiku Corpus, as well as of random 12-wordsequences from Project Gutenberg and from the NANC newswire corpus.
SourceAvg. Assoc. Relations (<3)
Avg. WordNet Relations (<4)
News0.262.02
Prose0.221.4
Haiku0.321.38
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Filling body with soul: Theme Selection
• Generating the seed of the story:– Start with a word
random walk on a word graphMany possible variants. We currently use:
start with the node of the seed word
do several short random walkskeep resulting word set
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water
flowerbutterfly
spring
fall fall
green
water
bloom
Spring {flower, butterfly…}
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Filling body with soul• For a given structure:
– Choose first line containing seed word– Choose other lines containing a word from the set
• This is adequate, but relations might be straightforward
Searching for a better soul: Generate several poems for the pattern
Rerank them based on associativity measure.Reranking catches further “residual” relations
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6alligator peara handful of whitesin the spring
8avocado peara kind of bootsin the fall
10pear salada season of tearsin the summer
10pear treea seasoning of spicesin the fall
10alligator peara spring of tearsin the blackness
NN NNDET_a NN of
NNSPP_in DET_the
NN
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Evaluation Method• ‘Turing test’:
– Was this Haiku written by human or by a computer?
– How would you grade it between 1 to 5?• Settings:
– AUTO Haiku set: 15 Haiku created by Gaiku without any manual selection, 10 random human Haiku on same subjects
– SEL set: 17 Haiku created by Gaiku, selected manually out several runs, 9 award winning human Haiku
• 52 subjects
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Results: AUTO set
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Results: SEL set
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The Best of Gaiku
• Best in SEL. Classified as human - 77.2%, average grade 3.09
• Best in AUTO. Classified as human - 72.2%, average grade 2.75
early dewthe water containsteaspoons of honey
cherry treepoisonous flowers lieblooming
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Conclusions• Word Association Norms have good potential
in creative content generation
Future Work: Lots!– Haiku: improve theme selection– Additional forms of creative texts
• Test WAN in general NLP tasks:– Use WAN for (Non-creative) Generation – Word Sense Disambiguation– Lexical chains– ‘Guess the word’ given associations (for people
with SLI)
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iced over pond
I skip a rock
the entire width
blossomlessbut not unloved the old magnolia
first date —the little pileof anchovies
fishing guidesboat in the backgrounda new trip
a holy cowa carton of milkseeking a church
blind snakeson the wet grasstombstoned terror
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iced over pond
I skip a rock
the entire width
blossomlessbut not unloved the old magnolia
fishing guidesboat in the backgrounda new trip
a holy cowa carton of milkseeking a church
blind snakeson the wet grasstombstoned terror
first date —the little pileof anchovies