Named entity recognition in Wikipedia

Balasuriya, Dominic; Ringland, Nicky; Nothman, Joel; Murphy, Tara; Curran, James

doi:10.3115/1699765.1699767

Cited by 82 publications

(45 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The three geoparsers that integrate external NER software (GeoTxt, Topocluster, CLAVIN), leave a lot of room for improvement as shown in the tables. Wikipedia text proved to be the greater challenge, a finding mirrored by Balasuriya et al (2009). The ultimate goal for geotagging performance should be F-Scores of 0.9?…”

Section: Scoring In Geotaggingmentioning

confidence: 99%

What’s missing in geographical parsing?

Gritta

Pilehvar

Limsopatham

et al. 2017

Lang Resources & Evaluation

124

View full text Add to dashboard Cite

Geographical data can be obtained by converting place names from freeformat text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.

show abstract

Section: Scoring In Geotaggingmentioning

confidence: 99%

What’s missing in geographical parsing?

Gritta

Pilehvar

Limsopatham

et al. 2017

Lang Resources & Evaluation

124

View full text Add to dashboard Cite

show abstract

“…NER often relies on a machine learning algorithm and an annotated dictionary (gazetteer). Several researchers have used Wikipedia for NER (Kazama & Torisawa 2007;Balasuriya et al 2009). Bunescu (2007) also aimed to derive new IE techniques with higher performance than existing ones using NER, named entity disambiguation and relation extraction.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

The People’s Encyclopedia Under the Gaze of the Sages: A Systematic Review of Scholarly Research on Wikipedia

et al. 2012

View full text Add to dashboard Cite

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 from various disciplines to study its content, communication and community dynamics from various perspectives. This article presents a systematic review of scholarly research on Wikipedia. We describe our detailed, rigorous methodology for identifying over 450 scholarly studies of Wikipedia. We present the WikiLit website (http://wikilit.referata.com), where most of the papers reviewed here are described in detail. In the major section of this article, we then categorize and summarize the studies. An appendix features an extensive list of resources useful for Wikipedia researchers.

show abstract

“…As a second contribution, we propose a series of feature combination and recombination to attain better results. We test our intuitions on both NER and WSI/WSD tasks and over four different corpora: CoNLL-2003 [17], WikiNER and Wikigold [4] for NER; Semeval-2007 [1] for WSI/WSD.…”

Section: Introductionmentioning

confidence: 99%

Fusion Techniques for Named Entity Recognition and Word Sense Induction and Disambiguation

2017

View full text Add to dashboard Cite

In this paper we explore the use of well-known multimodal fusion techniques to solve two prominent Natural Language Processing tasks. Specifically, we focus on solving Named Entity Recognition and Word Sense Induction and Disambiguation by applying feature-combination methods that have already shown their efficiency in the multimedia analysis domain. We present a series of experiments employing fusion techniques in order to combine textual linguistic features. Our intuition is that by combining different types of features we may find semantic relatedness among words at different levels and thus, the combination (and recombination) of these levels may yield gains in terms of metrics' performance. To our knowledge, employing these techniques has not been studied for the tasks we address in this paper. We test the proposed fusion techniques on three datasets for named entity recognition and one for word sense disambiguation and induction. Our results show that the combination of textual features indeed improves the performance compared to single feature representation and the trivial feature concatenation.

show abstract

Named entity recognition in Wikipedia

Cited by 82 publications

References 16 publications

What’s missing in geographical parsing?

What’s missing in geographical parsing?

The People’s Encyclopedia Under the Gaze of the Sages: A Systematic Review of Scholarly Research on Wikipedia

Fusion Techniques for Named Entity Recognition and Word Sense Induction and Disambiguation

Contact Info

Product

Resources

About