In this paper, the authors discuss research on whether they can use Mechanical Turk (MTurk) to acquire good annotations with respect to gold-standard data, whether they can filter out low-quality workers (spammers), and whether there is a learning effect associated with repeatedly completing the same kind of task.
This paper discusses anchor nodes placement for effective passive localization. The authors show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity.
This paper from the International Conference on Computational Science conference proceedings presents new methods that derive a new quality metric for automated scoring of quality of mucosa inspection performed by the endoscopist.
This paper introduces a new representation of sentences--Minimal Meaningful Propositions (MMPS), which allows significant improvement of the mapping between a learner's answer and the ideal response.
This paper describes a co-training approach that uses the text and citation information of a research article as two different views to predict the topic of an article.
This paper analyzes the performance of a domain-general sarcasm detection system on datasets from two different domains: Twitter and Amazon product reviews.
This paper presents an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications.
This paper describes a dialogue system framework for a companionable robot, which aims to guide patients towards health behavior changes via natural language analysis and generation.
This paper from the International Conference on Computational Science, ICCS 2011 conference proceedings describes a program profiling and analysis tool called Gleipnir.
This paper analyses user messages on an online cancer support community, Cancer Survivors Network (CSN), to identify the two types of social support present: emotional support and informational support.
This paper presents an approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems.
This paper describes an automatic question generator that uses semantic pattern recognition to create questions of varying depth and type for self-study or tutoring.
This paper analyzes the topic identification stage of single-document automatic text summarization across four different domains, consisting of newswire, literary, scientific and legal documents.
This paper presents the results of the Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, aiming to bring together researchers in educational NLP technology and textual entailment.
This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, the authors show that their methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features.
This paper shows results from an investigation whether a classifier can be taught to identify these constructions and consideration of the hypothesis that identifying construction types can improve the semantic interpretation of previously unseen predicate uses.
This paper proposes a new shared task on grading student answers with the goal of enabling well-targeted and flexible feedback in a tutorial dialogue setting.
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