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Publications |
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Pearl, L. &
Forsythe, H. (under review, updated 2/1/24).
Inaccurate representations, inaccurate deployment, or both? Using computational cognitive modeling to investigate the development of pronoun interpretation in Spanish.
Language. [lingbuzz].
Code available at github.
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Pearl, L. (under review, updated 12/19/23).
Minimalism for language acquisition.
In Kleanthes Grohmann & Evelina Leivada (eds.), The Cambridge Handbook of Minimalism and Its Applications. [lingbuzz]
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Attali, N., Scontras, G.,
Pearl, L., &
Wulff, S. (under review, updated 5/26/23).
Using world knowledge to interpret quantifier-scope ambiguity.
Language.
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Dickson, N., Futrell, R., & Pearl, L. (in press 1/25/24).
I Forgot but It’s Okay: Learning about Island Constraints under Child-Like Memory Constraints.
In Proceedings of the 48th annual Boston University Conference on Language Development.
[lingbuzz]
[code]
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Pearl, L. 2023.
Computational cognitive modeling for syntactic acquisition: Approaches that integrate information from multiple places.
Journal of Child Language, 50(6), 1353-1373. https://doi.org/10.1017/S0305000923000247 [lingbuzz]
[journal version]
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Pearl, L. 2023.
Modeling syntactic acquisition.
In J. Sprouse (ed.), Oxford Handbook of Experimental
Syntax, 209-270. [lingbuzz] Includes
future directions and annotated bibliography.
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Pearl, L. & Bates, A. 2022.
A new way to identify if variation in children's input could be developmentally meaningful:
Using computational cognitive modeling to assess input across socio-economic status for syntactic islands.
Journal of Child Language, 1-34. doi:10.1017/S0305000922000514. [lingbuzz].
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Attali, N., Pearl, L., & Scontras, G. 2022.
Corpus evidence for the role of world knowledge in ambiguity reduction: Using high positive expectations to inform quantifier scope.
In Proceedings of Experiments in Linguistic Meaning, 2.
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Dickson, N., Pearl, L., & Futrell, R. 2022.
Learning constraints on wh-dependencies by learning how to efficiently represent wh-dependencies:
A developmental modeling investigation with Fragment Grammars.
In Proceedings of the Society for Computation in
Linguistics, 5, Article 22. https://doi.org/10.7275/7fd4-fw49.
[lingbuzz].
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Pearl, L. 2021.
Poverty of the Stimulus Without Tears.
Language Learning and Development. doi: 10.1080/15475441.2021.1981908. [lingbuzz].
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Scontras, G. &
Pearl, L. 2021.
When pragmatics matters more for truth-value judgments:
An investigation of quantifier scope ambiguity.
Glossa: A Journal of General Linguistics, 6(1), doi: https://doi.org/10.16995/glossa.5724. [lingbuzz].
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Nguyen, E. &
Pearl, L. 2021.
The link between lexical semantic features and children's comprehension
of English be-passives.
Language Acquisition, 28(4), 433-450. [online] [lingbuzz].
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Pearl, L. 2021.
How statistical learning can play well with Universal Grammar.
In Nicholas Allott, Terje Lohndal & Georges Rey (eds.), Wiley-Blackwell Companion to Chomsky, 267-286. [lingbuzz]
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Attali, N., Scontras, G. & Pearl, L. 2021.
Pragmatic factors can explain variation in interpretation preferences for quantifier-negation utterances: A computational approach.
In Proceedings of the 43rd annual meeting of the Cognitive Science Society, Vienna, Austria: Cognitive Science Society.
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Attali, N., Scontras, G. & Pearl, L. 2021.
Every quantifier isn't the same: Informativity matters for ambiguity resolution in quantifier-negation sentences.
In Proceedings of the Society for Computation in
Linguistics, 4, 394-395.
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Pearl, L. & Sprouse, J. 2021.
The acquisition of linking theories: A Tolerance and
Sufficiency Principle approach to deriving UTAH and rUTAH.
Language Acquisition,
doi: 10.1080/10489223.2021.1888295. [lingbuzz].
Code
available at github
(derived-tolp subdirectory).
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Pearl, L. 2021.
Theory and predictions for the development of
morphology and syntax: A Universal Grammar + statistics approach.
Special issue of the Journal of Child Language, 48(5), 907-936.
doi: 10.1017/S0305000920000665.
[lingbuzz]
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Vogler, N. & Pearl, L. 2020.
Using linguistically-defined specific details to detect deception across domains.
Natural Language Engineering, 26(3), 349-373.
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Pearl, L. 2020.
Leveraging monolingual developmental techniques to better understand heritage languages.
Bilingualism: Language & Cognition 23(1), 39-40. https://doi.org/10.1017/S1366728919000361.
[link to official version]
[lingbuzz]
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Pearl, L. & Sprouse, J. 2019.
Comparing solutions to the linking problem using an integrated quantitative framework of language acquisition.
Language, 95(4), 583-611. [lingbuzz] Code
available at github.
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Forsythe, H. & Pearl, L. 2019.
Immature representation or immature deployment? Modeling child pronoun resolution.
In Proceedings of the Society for Computation in
Linguistics, 3, article 59.
[scholarworks]
[lingbuzz]
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Nyguen, E. & Pearl, L. 2019.
Using Developmental Modeling to Specify Learning and Representation of the Passive in English Children.
In Proceedings of the 43rd annual Boston University Conference on Language Development, Megan M. Brown and Brady Dailey (eds), Somerville, MA: Cascadilla Press, 469-482.
[lingbuzz]
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Bates, A. & Pearl, L. 2019.
*What do you think that happens?
A quantitative and cognitive modeling analysis of linguistic evidence across socioeconomic status for learning syntactic islands.
In Proceedings of the 43rd annual Boston University Conference on Language Development, Megan M. Brown and Brady Dailey (eds), Somerville, MA: Cascadilla Press, 42-56.
[lingbuzz]
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Pearl, L. 2019.
Fusion is great,
and interpretable fusion could be exciting
for theory generation.
Perspectives section of Language, 95(1), e109-e114. [lingbuzz].
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Bates, A.,
Pearl, L., and Braunwald, S. 2018.
I can believe it: Quantitative evidence for
closed-class category knowledge in an English-speaking 20-
to 24-month-old child.
In Proceedings of the Berkeley Linguistics
Society,
K. Garvin, N. Hermalin, M. Lapierre, Y. Melguy, T. Scott, & E. Wilbanks (eds),
1-16.
[lingbuzz]
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Bar-Sever G., Lee, R.,
Scontras, G., and Pearl, L. 2018.
Little lexical learners: Quantitatively assessing the development of adjective ordering preferences.
In Bertolini, A. & Kaplan, M. (eds), BUCLD 42 Proceedings, Somerville, MA:
Cascadilla Press, 58-71.
[lingbuzz]
[data (zip)]
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Savinelli, K., Scontras,
G., and Pearl, L. 2018.
Exactly two things to learn from modeling scope ambiguity resolution: Developmental continuity and numeral semantics.
In Proceedings of the 8th Workshop on Cognitive Modeling and Computational
Linguistics, Salt Lake City, UT.
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Pearl, L. & Phillips, L. 2018.
Evaluating language acquisition models: A utility-based
look at Bayesian segmentation.
In A. Villavicencio & T. Poibeau (eds), Language,
Cognition and Computational Models, Cambridge University
Press, 185-224.
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Nguyen, E.
and Pearl, L. 2018.
Do You Really Mean It?
Linking Lexical Semantic Profiles
and the Age of Acquisition for the English Passive.
In Proceedings of the 35th West Coast Conference on
Formal Linguistics, Wm. G. Bennett, Lindsay Hracs, and Dennis Ryan Storoshenko (eds), Somerville, MA: Cascadilla Proceedings. 288-295.
[lingbuzz]
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Savinelli, K., Scontras,
G., and Pearl, L. 2017.
Modeling scope ambiguity resolution as pragmatic inference: Formalizing differences in child and adult behavior.
In Proceedings of the 39th annual meeting of the
Cognitive Science Society, London, UK: Cognitive Science
Society, 3064-3069.
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Pearl, L., Scontras, G., & Singh, S. 2017.
Large-scale sophisticated linguistic monitoring.
A Decadal Survey of the Social and Behavioral Sciences for
National Security, Division of Behavioral and Social
Sciences and Education (DBASSE) of the National Academies.
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Pearl, L., Ho, T., &
Detrano, Z. 2017.
An argument from acquisition:
Comparing English metrical stress representations by how learnable they are from child-directed speech.
Language Acquisition, 24, 307-342.
[lingbuzz]
[data: xls][code:
zip and github]
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Pearl, L. 2017.
Evaluation, use, and refinement of knowledge
representations through acquisition modeling.
[lingbuzz]. Language
Acquisition, 24, 126-147.
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Bar-Sever, G. & Pearl, L. 2016.
Syntactic Categories Derived from Frequent Frames Benefit Early Language Processing in English and ASL.
Proceedings of the 40th annual Boston University
Conference on Language Development (ed. Jennifer Scott and Deb Waughtal), Somerville, MA:
Cascadilla Press, 32-46.
pre-print copy
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Pearl, L., & Mis, B. 2016.
The role of indirect positive evidence in syntactic
acquisition: A look at anaphoric
one. Supplementary material pdf. Language,
92(1), 1-30. [lingbuzz].
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Pearl, L., Lu, K., & Haghighi, A. 2016.
The Character in the Letter: Epistolary Attribution in
Samuel Richardson's Clarissa.
Digital Scholarship in
the Humanities, 32(2), 355-376. doi: 10.1093/llc/fqw007. (DSH
version)
[Clarissa Letters Dataset: zip]
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Pearl, L. & Goldwater, S. 2016.
Statistical Learning, Inductive Bias, and Bayesian Inference
in Language Acquisition, In J. Lidz, W. Snyder, & C. Pater
(eds), The Oxford Handbook of Developmental Linguistics, 664-695.
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Pearl, L. & Braunwald, S. 2015.
Review of Language in Mind: An Introduction to
Psycholinguistics by Julie Sedivy.
Teaching Linguistics subsection of Language,
91(4), e181-183.
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Phillips, L. & Pearl, L. 2015.
The utility of cognitive plausibility in language
acquisition modeling: Evidence from word
segmentation. Cognitive Science, 39(8), 1824-1854. doi:
10.1111/cogs.12217. [Code & corpora: github]
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Pearl, L., & Sprouse, J. 2015.
Computational modeling for language acquisition: A
tutorial with syntactic islands. Journal of Speech,
Language, and Hearing Research, 58, 740-753. doi: 10.1044/2015_JSLHR-L-14-0362. [lingbuzz] [JSLHR]
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Phillips, L. & Pearl, L. 2015.
Utility-based evaluation metrics for models of language
acquisition: A look at speech segmentation. Workshop on
Cognitive Modeling and Computational Linguistics 2015,
NAACL.
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Pearl, L. & Enverga, I. 2015.
Can you read my mindprint? Automatically identifying
mental states from language text using deeper linguistic
features. Interaction Studies, 15(3), 359-387.
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Pearl, L., Ho, T., & Detrano, Z. 2014.
More learnable than thou? Testing metrical phonology
representations with child-directed speech. Proceedings
of the Berkeley Linguistics Society, 398-422. [lingbuzz]
[data: xls][code:
zip and github]
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Pearl, L. 2014.
Evaluating learning strategy components: Being fair.
Language, 90(3), e107-e114. [lingbuzz]
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Phillips, L. & Pearl, L. 2014.
Bayesian inference as a viable cross-linguistic word
segmentation strategy: It's all about what's
useful. Proceedings of the 36th Annual Conference
of the Cognitive Science Society, Quebec City, CA:
Cognitive Science Society, 2775-2780.
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Phillips, L. & Pearl, L. 2014.
Bayesian inference as a cross-linguistic word
segmentation strategy: Always learning useful
things. Proceedings of the Computational and Cognitive
Models of Language Acquisition and Language Processing Workshop, EACL,
Gothenberg, Sweden, 9-13.
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Pearl, L. & Sprouse, J. 2013.
Computational Models of Acquisition for Islands, In J. Sprouse & N. Hornstein (eds), Experimental Syntax and Islands Effects. Cambridge University Press, 109-131.
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Pearl, L. & Steyvers, M. 2013.
"C'mon - You Should Read This": Automatic Identification of Tone from Language Text. International Journal of Computational Linguistics, 4(1), 12-30.
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Pearl, L. & Lidz, J. 2013.
Parameters in Language Acquisition. In C. Boeckx & K. Grohmann (eds), The Cambridge Handbook of Biolinguistics, Cambridge, UK: Cambridge University Press, 129-159.
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Pearl, L., & Sprouse, J. 2013.
Syntactic islands and learning biases: Combining experimental syntax and computational modeling to investigate the language acquisition problem. Language Acquisition, 20, 23-68. DOI 10.1080/10489223.2012.738742. [lingbuzz]
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Phillips, L. & Pearl, L. 2012.
'Less is More' in Bayesian word segmentation: When cognitively plausible learners outperform the ideal, In N. Miyake, D. Peebles, & R. Cooper (eds), Proceedings of the 34th Annual Conference of the Cognitive Science Society, 863-868. Austin, TX: Cognitive Science Society.
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Pearl, L., & Steyvers, M. 2012.
Detecting Authorship Deception: A Supervised Machine Learning Approach Using Author Writeprints, Literary and Linguistic Computing, 27(2), 183-196. DOI 10.1093/llc/fqs003.
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Pearl, L., Goldwater, S., & Steyvers, M. 2011.
Online Learning Mechanisms for Bayesian Models of Word Segmentation, Research on Language and Computation, special issue on computational models of language acquisition, 8(2), 107-132. DOI 10.1007/s11168-011-9074-5.
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Pearl, L., & Mis, B. 2011.
How Far Can Indirect Evidence Take Us? Anaphoric One Revisited, In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 879-884. Austin, TX: Cognitive Science Society.
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Pearl, L. 2011.
When unbiased probabilistic learning is not enough: Acquiring a parametric system of metrical phonology. Language Acquisition, 18(2), 87-120.
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Pearl, L. 2010.
Using computational modeling in language acquisition research, In E. Blom & S. Unsworth (eds). Experimental Methods in Language Acquisition Research, John Benjamins.
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Pearl, L. & Steyvers, M. 2010.
Identifying Emotions, Intentions, & Attitudes in Text Using a Game with a Purpose.
Proceedings of NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Los Angeles, CA: NAACL.
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Pearl, L., Goldwater, S., & Steyvers, M. 2010.
How Ideal Are We? Incorporating Human Limitations into Bayesian Models of Word Segmentation, In In K. Franich, K. Iserman, and L. Keil (eds), Proceedings of the 34th annual Boston University Conference on Child Language Development, Somerville, MA: Cascadilla Press, 315-326.
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Pearl, L. 2009.
Learning English Metrical Phonology: When Probability Distributions Are Not Enough, In Jean Crawford, Koichi Otaki, and Masahiko Takahashi (eds.), Proceedings of the 3rd Conference on Generative Approaches to Language Acquisition North America (GALANA 2008), Somerville, MA: Cascadilla Press, 200-211. (available through the Cascadilla Proceedings Project Website)
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Pearl, L. & Lidz, J. 2009.
When domain general learning fails and when it succeeds: Identifying the contribution of domain specificity, Language Learning and Development, 5(4), 235-265.
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Pearl, L. 2008.
Putting the Emphasis on Unambiguous: The Feasibility of
Data Filtering for Learning English Metrical Phonology, BUCLD 32: Proceedings of the
32nd annual Boston University Conference on Child Language Development, Chan, H., Jacob, H., and Kapia, E (eds.), Somerville, MA: Cascadilla Press, 390-401.
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Pearl, L. & Weinberg, A. 2007.
Input Filtering in Syntactic Acquisition: Answers from
Language Change Modeling, Language Learning and Development, 3(1), 43-72.
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Pearl, L. 2005.
The Input to Syntactic Acquisition: Solutions from Language Change
Modeling, Proceedings of Second Workshop on Psychocomputational Models of Human Language Acquisition, Ann Arbor, Michigan, 1-9.
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Pearl, L. 2005.
Addressing Acquisition from Language Change:
A Modeling Perspective, University of Pennsylvania Working Papers in Linguistics, 11.1.
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