Research Topics


Language Acquisition
Models of Acquirability
Linguistic Cues to Information
Foundations
Theoretical Linguistics
Natural Language Processing


Publications

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.
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]
Attali, N., Scontras, G., Pearl, L., & Wulff, S. (under review, updated 5/26/23). Using world knowledge to interpret quantifier-scope ambiguity. Language.
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]
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]
Pearl, L. 2023. Modeling syntactic acquisition. In J. Sprouse (ed.), Oxford Handbook of Experimental Syntax, 209-270. [lingbuzz] Includes future directions and annotated bibliography.
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].
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.
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].
Pearl, L. 2021. Poverty of the Stimulus Without Tears. Language Learning and Development. doi: 10.1080/15475441.2021.1981908. [lingbuzz].
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].
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].
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]
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.
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.
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).
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]
Vogler, N. & Pearl, L. 2020. Using linguistically-defined specific details to detect deception across domains. Natural Language Engineering, 26(3), 349-373.
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]
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.
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]
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]
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]
Pearl, L. 2019. Fusion is great, and interpretable fusion could be exciting for theory generation. Perspectives section of Language, 95(1), e109-e114. [lingbuzz].
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]
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)]
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.
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.
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]
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.
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.
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]
Pearl, L. 2017. Evaluation, use, and refinement of knowledge representations through acquisition modeling. [lingbuzz]. Language Acquisition, 24, 126-147.
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
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].

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]
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.
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.
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]
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]
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.
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.
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]
Pearl, L. 2014. Evaluating learning strategy components: Being fair. Language, 90(3), e107-e114. [lingbuzz]
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.
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.
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.
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.
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.
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]
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.
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.
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.
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.
Pearl, L. 2011. When unbiased probabilistic learning is not enough: Acquiring a parametric system of metrical phonology. Language Acquisition, 18(2), 87-120.
Pearl, L. 2010. Using computational modeling in language acquisition research, In E. Blom & S. Unsworth (eds). Experimental Methods in Language Acquisition Research, John Benjamins.
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.
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.
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)
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.
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.
Pearl, L. & Weinberg, A. 2007. Input Filtering in Syntactic Acquisition: Answers from Language Change Modeling, Language Learning and Development, 3(1), 43-72.
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.
Pearl, L. 2005. Addressing Acquisition from Language Change: A Modeling Perspective, University of Pennsylvania Working Papers in Linguistics, 11.1.


Other Useful Things

Tips & best practices for presentation design (.m4v) (youtube): Greg Scontras, the winner of the LSA 2017 Five-Minute Linguist presentation contest, kindly put this 25 minute presentation together in March 2017 concerning things worth thinking about when designing presentations. It's an excellent resource, and absolutely worth a listen & watch before you put your presentation together. It includes a breakdown of his winning 5 minute presentation, as well as general tips.
CHILDES Treebank: A collection of child-directed speech from the CHILDES database annotated with phrase structure trees is available here (.zip file). This was generated as part of NSF grant BCS-0843896, "Testing the Universal Grammar Hypothesis", which you can find out more about here, and NSF grant BCS-1347028, "An integrated theory of syntactic acquisition", which you can findout more about here.
Clarissa Letters Dataset: A collection of all character letters from Samuel Richardson's epistolary novel Clarissa is available here (zipped file). Used for the authorship analyses in Pearl, Lu, & Haghighi (2016).
Metrical Phonology Compatibility Code: Code for conducting the compatibility analyses described in work by Pearl, Ho, & Detrano is available here (zipped file) and at github.
Pearl_Brent Phonemes corpus: An English child-directed speech corpus of words transcribed into phonemes is available here (zipped file). This corpus was created from the English Brent corpus of CHILDES and can be used for word segmentation studies. This is also available through the derived corpus section of the CHILDES database.
Phillips-Pearl Cross-linguistic Segmentation Corpus: A collection of child-directed speech from seven languages transcribed phonemically and divided into syllables is available here (zipped file). This corpus was created from several corpora from CHILDES and can be used for speech segmentation studies. This is also available through the github.
Susan R. Braunwald Language Acquisition Diaries: Susan R. Braunwald's career as a parent-diarist began in 1969 when she decided to combine her personal wish to care for her first child, L's older sister J, with an equally pressing need to be intellectually productive. She kept and analyzed a language acquisition diary on J that made her wonder why, when, and how the process of language development occurred. These questions were so intriguing that Braunwald decided to keep a second language acquisition diary that was scientifically rigorous and compensated for the pitfalls of the diary method. She was an experienced parent-diarist and the design, collection, and analysis of the diary study on L, which began in 1971, became her lifelong professional commitment.
UCI_Brent Syllabic corpus: An English child-directed speech corpus of words transcribed into phonemes and divided into syllables is available here (zipped file). This corpus was created from a subsection of the English Brent corpus of CHILDES directed at children 9 months and younger and can be used for word segmentation studies. It also includes the conversion scripts used so this process can be applied to other corpora of interest. This is also available through the derived corpus section of the CHILDES database.
WordSleuth corpus: Data from the WordSleuth game-with-a-purpose (GWAP), which identifies mental states in language text, is available here (zipped file). Last updated 6/7/18.