Department of Computer Science M.Sc.(Computer Science) IV ...
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Erin GrantDepartment of Computer Science
University of Toronto
Gender differences in languageproduction and child-directed
speech
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ComputationalLinguistics Cognitive
Science
ArtificialIntelligence
TheoreticalLinguistics
Psycho-linguistics
NaturalLanguageProcessing Human-
computerInteraction
MachineLearning
Machinetranslation
What is theintersection
betweencomputer science
andnatural language?
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Computationalmodelling
of languageacquisition
Why is thisan interesting
problem?
How cancomputer
science help?
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Differences between female and male speech
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Differences between female and male speech
Some typical examples:
word frequency
male speech female speechexpletives diminutives
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assertions vs. hedges
male speech female speechThat is . This is , isn’t it?
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politeness register
male speech female speechdirect requests modals: “could”, “would” && commands politeness markers: “please”
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We see systematic differences in the language that childrenproduce when they are young.
“Language development is a domain in which genderdifferences are already well established. . . the [difference] inverbal ability is one of the few gender differences considered tobe robust.” 1
1Lovas, Gretchen. 2011. “Gender and Patterns of Language Development in Mother-Toddler and
Father-Toddler Dyads.” First Language 31 (1): 83-108.
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complexity
male speech female speechsmaller vocabulary2 greater syntactic complexity3
2Huttenlocher, Janellen, Wendy Haight, Anthony Bryk, Michael Seltzer, and Thomas Lyons. 1991. “Early
Vocabulary Growth: Relation to Language Input and Gender.” Developmental Psychology 27 (2): 236-248.3
Bornstein, Marc H., Chun-Shin Hahn, and O. Maurice Haynes. “Specific and general language performanceacross early childhood: Stability and gender considerations.” First Language 24, no. 3 (2004): 267-304.
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frequency of abstractions4
male speech female speechabstract concrete
4Newman, Matthew L., Carla J. Groom, Lori D. Handelman, and James W. Pennebaker. “Gender differences
in language use: An analysis of 14,000 text samples.” Discourse Processes 45, no. 3 (2008): 211-236.
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function: what is the goal of the speech act?56
male speech female speechinstrumental end-in-itselfdeclarative social
5Herring, S. C. (1993). “Gender and democracy in computer-mediated communication.” Electronic Journal
of Communication, 3(2).6
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. (2003). “Psychological aspects of natural language use:Our words, our selves.” Annual Review of Psychology, 54, 547577.
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Our hypothesis:
The difference can be attributed to the language that thechild is exposed to during the acquisition phase.
Our prediction:
There will be variation in child-directed speech betweenmale and female groups.
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The data we are working with. . .
Childes
663,239 utterances
3,206,502 words
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We divide the CDS data into four groups:
male speaker to female child female speaker to female childmale speaker to male child female speaker to male child
Can we detect variation between the groups?
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Measures
I Word-level measures
I lexical content of thewords:
I emotivityI concreteness
I word frequency
I Phrase-level measures
I Context-level measures
“I wonder whether the teacherwill get mad at me.”
Iself-reference
wondercognitive process; abstraction
whetheruncertainty theteacherreference to school will getmademotive language: anger atmeself-reference.
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Measures
I Word-level measures
I Phrase-level measures
I type-to-token ratioI structural frequenciesI mean
length-of-utterance(MLU)
I Context-level measures
“Let’s try to be quiet.”
let us try to be quiet .
ROOT
COMP
SUBJ
XCOMP
XSUBJ
AUX
COP
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Measures
I Word-level measures
I Phrase-level measures
I Context-level measures
I topic and surroundingcontext
I function of the speechact
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Preliminary results
I MLU (mean length-of-utterance) is higher in speechdirected towards male children
I word type-to-token ratio is higher in speech directedtowards female children
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Over the course of the summer. . .
We will:
I determine a method to test the statistical significance ofthese results.
I develop new measures to analyse potential differences.
I find out if our prediction is accurate!
I apply significant features to create a gender classificationalgorithm that can be used on arbitrary text.
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