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Erin GrantDepartment of Computer Science
University of Toronto
Gender differences in languageproduction and child-directed
speech
ComputationalLinguistics Cognitive
Science
ArtificialIntelligence
TheoreticalLinguistics
Psycho-linguistics
NaturalLanguageProcessing Human-
computerInteraction
MachineLearning
Machinetranslation
What is theintersection
betweencomputer science
andnatural language?
Computationalmodelling
of languageacquisition
Why is thisan interesting
problem?
How cancomputer
science help?
Differences between female and male speech
Differences between female and male speech
Some typical examples:
word frequency
male speech female speechexpletives diminutives
assertions vs. hedges
male speech female speechThat is . This is , isn’t it?
politeness register
male speech female speechdirect requests modals: “could”, “would” && commands politeness markers: “please”
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.
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.
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.
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.
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.
The data we are working with. . .
Childes
663,239 utterances
3,206,502 words
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?
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.
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
Measures
I Word-level measures
I Phrase-level measures
I Context-level measures
I topic and surroundingcontext
I function of the speechact
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
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.