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F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then(More)
Sum product networks (SPNs) are a new class of deep proba-bilistic models. They can contain multiple hidden layers while keeping their inference and training times tractable. An SPN consists of interleaving layers of sum nodes and product nodes. A sum node can be interpreted as a hidden variable, and a product node can be viewed as a feature capturing rich(More)
This paper presents a maximum entropy-based named entity recognizer (NER). It differs from previous machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects(More)
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the classifier. It is often used in situations where labeled training data is scarce but unlabeled data is abundant. In this paper, we consider the problem of domain(More)
Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems. However, only dependencies between adjacent labels are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer distance dependencies are taken into account. In this paper,(More)
This paper describes how a machine-learning named entity recognizer (NER) on upper case text can be improved by using a mixed case NER and some unlabeled text. The mixed case NER can be used to tag some unlabeled mixed case text, which are then used as additional training material for the upper case NER. We show that this approach reduces the performance(More)
In this paper, we present a classification-based approach towards single-slot as well as multi-slot information extraction (IE). For single-slot IE, we worked on the domain of Seminar Announcements, where each document contains information on only one seminar. For multi-slot IE, we worked on the domain of Management Succession. For this domain, we restrict(More)