Libby Barak


Modeling the Acquisition of Words with Multiple Meanings
Libby Barak | Sammy Floyd | Adele Goldberg
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

Polysemous Language in Child Directed Speech
Sammy Floyd | Libby Barak | Adele Goldberg | Casey Lew-Williams
Proceedings of the 2019 Workshop on Widening NLP

Polysemous Language in Child Directed Speech Learning the meaning of words is one of the fundamental building blocks of verbal communication. Models of child language acquisition have generally made the simplifying assumption that each word appears in child-directed speech with a single meaning. To understand naturalistic word learning during childhood, it is essential to know whether children hear input that is in fact constrained to single meaning per word, or whether the environment naturally contains multiple senses.In this study, we use a topic modeling approach to automatically induce word senses from child-directed speech. Our results confirm the plausibility of our automated analysis approach and reveal an increasing rate of using multiple senses in child-directed speech, starting with corpora from children as early as the first year of life.

Evaluating Ways of Adapting Word Similarity
Libby Barak | Adele Goldberg
Proceedings of the 2019 Workshop on Widening NLP

People judge pairwise similarity by deciding which aspects of the words’ meanings are relevant for the comparison of the given pair. However, computational representations of meaning rely on dimensions of the vector representation for similarity comparisons, without considering the specific pairing at hand. Prior work has adapted computational similarity judgments by using the softmax function in order to address this limitation by capturing asymmetry in human judgments. We extend this analysis by showing that a simple modification of cosine similarity offers a better correlation with human judgments over a comprehensive dataset. The modification performs best when the similarity between two words is calculated with reference to other words that are most similar and dissimilar to the pair.

Context Effects on Human Judgments of Similarity
Libby Barak | Noe Kong-Johnson | Adele Goldberg
Proceedings of the 2019 Workshop on Widening NLP

The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.


Comparing Computational Cognitive Models of Generalization in a Language Acquisition Task
Libby Barak | Adele E. Goldberg | Suzanne Stevenson
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


Learning Verb Classes in an Incremental Model
Libby Barak | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics


Acquisition of Desires before Beliefs: A Computional Investigation
Libby Barak | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Seventeenth Conference on Computational Natural Language Learning


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Modeling the Acquisition of Mental State Verbs
Libby Barak | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)


Text Categorization from Category Name via Lexical Reference
Libby Barak | Ido Dagan | Eyal Shnarch
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

Extracting Lexical Reference Rules from Wikipedia
Eyal Shnarch | Libby Barak | Ido Dagan
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP