Lisa Pearl


Learning constraints on wh-dependencies by learning how to efficiently represent wh-dependencies: A developmental modeling investigation with Fragment Grammars
Niels Dickson | Lisa Pearl | Richard Futrell
Proceedings of the Society for Computation in Linguistics 2022


Immature representation or immature deployment? Modeling child pronoun resolution
Hannah Forsythe | Lisa Pearl
Proceedings of the Society for Computation in Linguistics 2020


Exactly two things to learn from modeling scope ambiguity resolution: Developmental continuity and numeral semantics
K.J. Savinelli | Greg Scontras | Lisa Pearl
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)


Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation
Lawrence Phillips | Lisa Pearl
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics


Bayesian inference as a cross-linguistic word segmentation strategy: Always learning useful things
Lawrence Phillips | Lisa Pearl
Proceedings of the 5th Workshop on Cognitive Aspects of Computational Language Learning (CogACLL)


Identifying Emotions, Intentions, and Attitudes in Text Using a Game with a Purpose
Lisa Pearl | Mark Steyvers
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text


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The Input for Syntactic Acquisition: Solutions from Language Change Modeling
Lisa Pearl
Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition


DUSTer: a method for unraveling cross-language divergences for statistical word-level alignment
Bonnie Dorr | Lisa Pearl | Rebecca Hwa | Nizar Habash
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers

The frequent occurrence of divergenceS—structural differences between languages—presents a great challenge for statistical word-level alignment. In this paper, we introduce DUSTer, a method for systematically identifying common divergence types and transforming an English sentence structure to bear a closer resemblance to that of another language. Our ultimate goal is to enable more accurate alignment and projection of dependency trees in another language without requiring any training on dependency-tree data in that language. We present an empirical analysis comparing the complexities of performing word-level alignments with and without divergence handling. Our results suggest that our approach facilitates word-level alignment, particularly for sentence pairs containing divergences.


Mapping Lexical Entries in a Verbs Database to WordNet Senses
Rebecca Green | Lisa Pearl | Bonnie J. Dorr | Philip Resnik
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics