Exploiting Wikipedia as External Knowledge for Named Entity Recognition

By Jun'ichi Kazama, Kentaro Torisawa. Conference on Empirical Methods in Natural Language Processing, 2007

346 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1fc360befecd3ca2fa9bddd799e4c16211299fa3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Fine-grained location extraction from tweets with temporal awareness (2014, methodology): “A similar strategy was used for NER in [8].”
TwiNER: named entity recognition in targeted twitter stream (2012, methodology): “[7] only looks for phrases of no more than eight words that start with a word containing at least one capitalized letter in a sentence, and treats phrases with corresponding Wikipedia page as named entities.”
Evaluating the Inferential Utility of Lexical-Semantic Resources (2009, methodology): “Wikipedia ﬁrst sentence ( WikiFS ) : Kazama and Torisawa (2007) used Wikipedia as an exter-nal knowledge to improve Named Entity Recognition.”
A Joint Model for Entity Analysis: Coreference, Typing, and Linking (2014, methodology): “Named entity recognition is improved by simple coreference (Finkel et al., 2005; Ratinov and Roth, 2009) and knowledge from Wikipedia (Kazama and Torisawa, 2007; Ratinov and Roth, 2009; Nothman et al., 2013; Sil and Yates, 2013).”
Cross-lingual Name Tagging and Linking for 282 Languages (2017, methodology): “…features for IE: Wikipedia pages have been used as additional features to improve various Information Extraction (IE) tasks, including name tagging (Kazama and Torisawa, 2007), coreference resolution (Paolo Ponzetto and Strube, 2006), relation extraction (Chan and Roth, 2010) and event extraction…”
Knowledge-Based Textual Inference via Parse-Tree Transformations (2015, background): “Recently, several works aimed to extract lexical-semantic knowledge from Wikipedia, using its metadata, as well as textual definitions (Kazama & Torisawa, 2007; Ponzetto & Strube, 2007; Shnarch, Barak, & Dagan, 2009; Lehmann et al., 2009, and others).”
Piggyback: Using Search Engines for Robust Cross-Domain Named Entity Recognition (2011, background): “Another source of world knowledge for NER is Wikipedia: Kazama and Torisawa (2007) show that pseudocategories extracted from Wikipedia help for in-domain NER.”
Design Challenges and Misconceptions in Named Entity Recognition (2009, methodology): “Recently, (Toral and Munoz, 2006; Kazama and Torisawa, 2007a) have successfully constructed high quality and high coverage gazetteers from Wikipedia.”
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, methodology): “Although many methods coupled with either discrete shallow features (Zhou and Su, 2002; Finkel et al., 2005; Torisawa et al., 2007) or continuous deep features (Lample et al., 2016; Ma and Hovy, (a).”
DBpedia - A crystallization point for the Web of Data (2009, unknown): “Further potential for the DBpedia extraction as well as for the NLP-eld in general lies in the idea to use huge bodies of background knowledge | like DBpedia | to improve the results of NLP-algorithms [ 11 ,8].”
Learning multilingual named entity recognition from Wikipedia (2013, background): “• Definition noun: Since many of Wikipedia’s articles begin with a definition, we extract the head unigram or bigram following a copula, if any, from the first sentence, following [31].”