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The ACL Anthology is a digital archive of conference and journal papers in natural language processing and computational linguistics. Its primary purpose is to serve as a reference repository of research results, but we believe that it can also be an object of study and a platform for research in its own right. We describe an enriched and standardized(More)
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(More)
Imagine two identical people receive exactly the same training on how to classify certain objects. Perhaps surprisingly, we show that one can then manipulate them into classifying some test items in opposite ways, simply depending on what other test items they are asked to classify (without label feedback). We call this the Test-Item Effect, which can be(More)
The ACL Anthology is a large collection of research papers in computational linguistics. Citation data was obtained using text extraction from a collection of PDF files with significant manual post-processing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks(More)
The Clair library is intended to simplify a number of generic tasks in Natural Language Processing (NLP), Information Retrieval (IR), and Network Analysis. Its architecture also allows for external software to be plugged in with very little effort. Functionality native to Clairlib includes Tokenization, Summarization, LexRank, Biased LexRank, Document(More)
When the distribution of unlabeled data in feature space lies along a manifold, the information it provides may be used by a learner to assist classification in a semi-supervised setting. While manifold learning is well-known in machine learning, the use of manifolds in human learning is largely unstudied. We perform a set of experiments which test a(More)
We consider the task of human collaborative category learning , where two people work together to classify test items into appropriate categories based on what they learn from a training set. We propose a novel collaboration policy based on the Co-Training algorithm in machine learning, in which the two people play the role of the base learners. The policy(More)
In a categorization task involving both labeled and unlabeled data, it has been shown that humans make use of the underlying distribution of the unlabeled examples. It has also been shown that humans are sensitive to shifts in this distribution, and will change predicted classifications based on these shifts. It is not immediately obvious what causes these(More)
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