We automatically create enormous, free and multilingual silver-standard training annotations
for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner …

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing
community of researchers who recognize it as a resource of exceptional scale and utility. It …

Wikipedia has become one of the ten most visited sites on the Web, and the world's leading
source of Web reference information. Its rapid success has inspired hundreds of scholars …

Support vector machines (SVMs) have become a popular tool for machine learning with
large amounts of high dimensional data. In this paper an approach for incremental learning …

Although primarily an encyclopedia, Wikipedia's expansive content provides a knowledge
base that has been continuously exploited by researchers in a wide variety of domains. This …

In this paper, we present a face annotation system to automatically collect and label celebrity
faces from the web. With the proposed system, we have constructed a large-scale dataset …

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in
the Linking Open Data cloud. We propose and compare various methods for addressing this …

An important application of semantic web technology is recognizing human-defined
concepts in text. Query transformation is a strategy often used in search engines to derive …

The DBpedia project extracts structured information from Wikipedia and makes it available
on the web. Information is gathered mainly with the help of infoboxes that contain structured …

This paper discusses a system that extracts and displays temporal and geospatial entities in
text. The first task involves identification of all events in a document followed by identification …