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Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semisupervised models for modeling human categorization.Keywords: Category learning; Semi-supervised learning; Machine learning Cognitive psychology has long had an interest in understanding human categorization: how we come to conceive of objects in the world as belonging to different categories, and how we use categories to draw inferences about the unobserved properties of objects. Toward this end, one of the most commonly used experimental paradigms has been supervised category learning: On each trial, the participant views a stimulus and must guess to which of a small number of categories it belongs. Feedback is provided that indicates either whether the guess was correct or what the correct answer was-the learning is supervised in this sense. The experimenter then measures how rapidly the participant learns to generate correct inferences about category membership, and how the acquired knowledge generalizes to novel stimuli.Correspondence should be sent to Bryan R. Gibson,

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The ACL Anthology is a large collection of research papers in computational linguistics. Citation data were obtained using text extraction from a collection of PDF files with significant manual postprocessing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks of paper citations, author citations, and author collaborations in an attempt to identify the most central papers and authors. The analysis includes general network statistics, PageRank, metrics across publication years and venues, the impact factor and h-index, as well as other measures.

A wish is "a desire or hope for something to happen." In December 2007, people from around the world offered up their wishes to be printed on confetti and dropped from the sky during the famous New Year's Eve "ball drop" in New York City's Times Square. We present an in-depth analysis of this collection of wishes. We then leverage this unique resource to conduct the first study on building general "wish detectors" for natural language text. Wish detection complements traditional sentiment analysis and is valuable for collecting business intelligence and insights into the world's wants and desires. We demonstrate the wish detectors' effectiveness on domains as diverse as consumer product reviews and online political discussions.