Will Radford

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Will Radford is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Evaluating entity linking with wikipedia Disambiguation
Information extraction
Named Entity Linking
Semi-structured resources
Wikipedia
Artificial Intelligence English 2013 Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate entities and then disambiguate them, returning either the best candidate or nil. However, comparison has focused on disambiguation accuracy, making it difficult to determine how search impacts performance. Furthermore, important approaches from the literature have not been systematically compared on standard data sets. We reimplement three seminal nel systems and present a detailed evaluation of search strategies. Our experiments find that coreference and acronym handling lead to substantial improvement, and search strategies account for much of the variation between systems. This is an interesting finding, because these aspects of the problem have often been neglected in the literature, which has focused largely on complex candidate ranking algorithms. © 2012 Elsevier B.V. All rights reserved. 0 0
Learning multilingual named entity recognition from Wikipedia Annotated corpora
Information extraction
Named entity recognition
Semi-structured resources
Semi-supervised learning
Wikipedia
Artificial Intelligence English 2013 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 systems rely on statistical models of annotated data to identify and classify names of people, locations and organisations in text. This dependence on expensive annotation is the knowledge bottleneck our work overcomes. We first classify each Wikipedia article into named entity (ne) types, training and evaluating on 7200 manually-labelled Wikipedia articles across nine languages. Our cross-lingual approach achieves up to 95% accuracy. We transform the links between articles into ne annotations by projecting the target articles classifications onto the anchor text. This approach yields reasonable annotations, but does not immediately compete with existing gold-standard data. By inferring additional links and heuristically tweaking the Wikipedia corpora, we better align our automatic annotations to gold standards. We annotate millions of words in nine languages, evaluating English, German, Spanish, Dutch and Russian Wikipedia-trained models against conll shared task data and other gold-standard corpora. Our approach outperforms other approaches to automatic ne annotation (Richman and Schone, 2008 [61], Mika et al., 2008 [46]) competes with gold-standard training when tested on an evaluation corpus from a different source; and performs 10% better than newswire-trained models on manually-annotated Wikipedia text. © 2012 Elsevier B.V. All rights reserved. 0 0
Graph-based named entity linking with Wikipedia Entity resolution
Integration
Text mining
Web intelligence
Wikipedia
Lecture Notes in Computer Science English 2011 Named entity linking (NEL) grounds entity mentions to their corresponding Wikipedia article. State-of-the-art supervised NEL systems use features over the rich Wikipedia document and link-graph structure. Graph-based measures have been effective over WordNet for word sense disambiguation (wsd). We draw parallels between NEL and (wsd), motivating our unsupervised NEL approach that exploits the Wikipedia article and category link graphs. Our system achieves 85.5% accuracy on the TAC 2010 shared task - competitive with the best supervised and unsupervised systems. 0 0
Graph-based named entity linking with wikipedia Entity resolution
Integration
Text mining
Web intelligence
Wikipedia
WISE English 2011 0 0