IdentityRank: Named Entity Disambiguation in the Context of the NEWS Project

Fernández, Norberto; Blázquez, José María; Sánchez, Luis; Bernardi, Ansgar

doi:10.1007/978-3-540-72667-8_45

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…The statistical method presented in [12], although it leverages the co-occurrence relation between NEs with different classes, is semi-automatic and uses user feedback to disambiguated results for updating heuristics and rules considered as a training dataset. In [20], some pattern matching rules written in JAPE's grammar ( [19]) are applied to resolve simple ambiguous cases in a text, based on the prefix or suffix of a name.…”

Section: Related Workmentioning

confidence: 99%

“…Closely related works are represented in [12], [18], [20], [27], and [29]. Some of those use heuristic rules and focus on only one type of NE such as location ( [27], [29]), or person ( [18]).…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, currently almost all approaches to NE disambiguation are either semi-automatic ( [12]) or primarily use heuristics and pattern matching for recognizing and disambiguating only one type of entities such as geographical entities ( [27], [29]), or persons ( [18]). The machine learning approach to disambiguation of NEs with respect to a given ontology and a KB has been explored surprisingly little, due to the shortage of training data.…”

Section: Introductionmentioning

confidence: 99%

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Named entity disambiguation on an ontology enriched by Wikipedia

Nguyen

Cao

2008

2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologie

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Currently, for named entity disambiguation, the shortage of training data is a problem. This paper presents a novel method that overcomes this problem by automatically generating an annotated corpus based on a specific ontology. Then the corpus was enriched with new and informative features extracted from Wikipedia data. Moreover, rather than pursuing rule-based methods as in literature, we employ a machine learning model to not only disambiguate but also identify named entities. In addition, our method explores in details the use of a range of features extracted from texts, a given ontology, and Wikipedia data for disambiguation. This paper also systematically analyzes impacts of the features on disambiguation accuracy by varying their combinations for representing named entities. Empirical evaluation shows that, while the ontology provides basic features of named entities, Wikipedia is a fertile source for additional features to construct accurate and robust named entity disambiguation systems.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Named entity disambiguation on an ontology enriched by Wikipedia

Nguyen

Cao

2008

2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologie

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“…Fortunately, news items contain always information about the city and the country yielding accurate recognition of the location mentioned in the story. We are currently implementing and evaluating more sophisticated disambiguation heuristics such as the IdentityRank algorithm [8] and a hybrid statistical approach [9] to minimize the disambiguation errors. When the accuracy is the primary concern, we envision a semi-automatic approach where suggestions will be proposed to the journalist during the annotation process.…”

Section: Enriching News Metadata With the Linked Data Cloudmentioning

confidence: 99%

Automatic metadata enrichment in news production

Mannens

Troncy

Braeckman

et al. 2009

2009 10th Workshop on Image Analysis for Multimedia Interactive Services

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News production is characterized by complex and dynamic workflows in which it is important to produce and distribute news items as fast as possible. In this paper, we show how personalized distribution and consumption of news items can be enabled by automatically enriching news metadata with open linked datasets available on the Web of data, thus providing a more pleasant experience to fastidious consumers where news content is presented within a broader historical context. Further we present a faceted browser that provides a convenient way for exploring news items based on an ontology of NewsML-G2 and rich semantic metadata.

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“…Australia as been typed as a Person. More sophisticated disambiguation heuristics such as IdentityRank [8] can be further employed to minimize these errors.…”

Section: Datasetmentioning

confidence: 99%

Bringing the IPTC News Architecture into the Semantic Web

Troncy

2008

Lecture Notes in Computer Science

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Abstract. For easing the exchange of news, the International Press Telecommunication Council (IPTC) has developed the NewsML Architecture (NAR), an XML-based model that is specialized into a number of languages such as NewsML G2 and EventsML G2. As part of this architecture, specific controlled vocabularies, such as the IPTC News Codes, are used to categorize news items together with other industry-standard thesauri. While news is still mainly in the form of text-based stories, these are often illustrated with graphics, images and videos. Media-specific metadata formats, such as EXIF, DIG35 and XMP, are used to describe the media. The use of different metadata formats in a single production process leads to interoperability problems within the news production chain itself. It also excludes linking to existing web knowledge resources and impedes the construction of uniform end-user interfaces for searching and browsing news content.In order to allow these different metadata standards to interoperate within a single information environment, we design an OWL ontology for the IPTC News Architecture, linked with other multimedia metadata standards. We convert the IPTC NewsCodes into a SKOS thesaurus and we demonstrate how the news metadata can then be enriched using natural language processing and multimedia analysis and integrated with existing knowledge already formalized on the Semantic Web. We discuss the method we used for developing the ontology and give rationale for our design decisions. We provide guidelines for re-engineering schemas into ontologies and formalize their implicit semantics. In order to demonstrate the appropriateness of our ontology infrastructure, we present an exploratory environment for searching and browsing news items.

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IdentityRank: Named Entity Disambiguation in the Context of the NEWS Project

Cited by 9 publications

References 7 publications

Named entity disambiguation on an ontology enriched by Wikipedia

Named entity disambiguation on an ontology enriched by Wikipedia

Automatic metadata enrichment in news production

Bringing the IPTC News Architecture into the Semantic Web

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