This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use

In this paper, we describe a new method for the problem of named entity classifica-tion for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names

The task of classifying entities appearing in textual annotations to an arbitrary set of classes has not been extensively researched, yet it is useful in multimedia retrieval. We proposed an unsupervised algorithm, which expresses entities and classes as Wordnet synsets and uses Lin measure

This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled corpus. This enables us to classify named entities without the cost for building a large

This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled corpus. This enables us to classify named entities without the cost for building a large hand

Situation entities (SEs) are the events, states, generic statements, and embedded facts and propositions introduced to a discourse by clauses of text. We report on the first datadriven models for labeling clauses according to the type of SE they introduce. SE classification is important

Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e

, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from

using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. Results: We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues

grouping of textual or partially textual entities. Document retrieval, categorization, routing, filtering, and clustering, as well as natural language processing tasks such as tagging, word sense disambiguation, and some aspects of understanding can be formulated as text classification. As the amount