2008 TransformingWikipediaIntoNERTrainData
- (Nothman et al., 2008) ⇒ Joel Nothman, James R. Curran, and Tara Murphy. (2008). “Transforming Wikipedia into Named Entity Training Data.” In: Proceedings of the Australasian Language Technology Workshop, 2008.
Subject Headings: Self-Supervised Learning Algorithm, Supervised Named Entity Recognition Algorithm, Wikipedia.
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Abstract
Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated text. We transform Wikipedia’s links into named entity annotations by classifying the target articles into common entity types (e.g. person, organisation and location). Comparing to MUC, CONLL and BBN corpora, Wikipedia generally performs better than other cross-corpus train/test pairs.
1 Introduction
Named Entity Recognition (NER), the task of identifying and classifying the names of people, organisations, locations and other entities within text, is central to many NLP tasks. The task developed from information extraction in the Message Understanding Conferences (MUC) of the 1990s. By the final two MUC evaluations, NER had become a distinct task: tagging the aforementioned proper names and some temporal and numerical expressions (Chinchor, 1998).
The CONLL NER evaluations of 2002 and 2003 (Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003) focused on determining superior machine learning algorithms and feature models for multilingual NER, marking tags for person (PER), organisation (ORG), location (LOC) and miscellaneous (MISC; broadly including e.g. events, artworks and nationalities). Brunstein (2002) and Sekine et al. (2002). expanded this into fine-grained categorical hierarchies; others have utilised theWordNet noun hierarchy (Miller, 1998) in a similar manner (e.g. Toral et al. (2008)). For some applications, such as biotextmining (Kim et al., 2003) or astroinformatics (Murphy et al., 2006), domain-specific entity classification schemes are more appropriate. Statistical machine learning systems have proved successful for NER. These learn terms and patterns commonly associated with particular entity classes, making use of many contextual, orthographic, linguistic and external knowledge features. They rely on annotated training corpora of newswire text, each typically smaller than a million words. The need for costly, low-yield, expert annotation therefore hinders the creation of more task-adaptable, high-performance named entity (NE) taggers.
This paper presents the use of Wikipedia — an enormous and growing, multilingual, free resource — to create NE-annotated corpora. We transform links between encyclopaedia articles into named entity annotations (see Figure 1). Each new term or name mentioned in a Wikipedia article is often linked to an appropriate article. A sentence introducing Ian Fleming’s novel Thunderball about the character James Bond may thus have links to separate articles about each entity. Cues in the linked article about Ian Fleming indicate that it is about a person, and the article on Thunderball states that it is a novel. The original sentence can then be automatically annotated with these facts. Millions of sentences may similarly be extracted fromWikipedia to form an enormous corpus for NER training.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2008 TransformingWikipediaIntoNERTrainData | James R. Curran Joel Nothman Tara Murphy | Transforming Wikipedia into Named Entity Training Data | http://www.aclweb.org/anthology/U/U08/U08-1016.pdf |