The Semantics of Negation Detection in Archaeological Grey Literature

Vlachidis, Andreas; Tudhope, Douglas

doi:10.1007/978-3-319-03437-9_20

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The algorithm is modified to enable negation detection in the archaeology domain. The adaptation strategy considered issues relating to the size of negation window, applicability and enhancement of negation moderator glossaries, and characteristics of pseudo negation moderators affecting the scope of negation phrases (for details see Vlachidis & Tudhope, ).…”

Section: Optima Pipelinementioning

confidence: 99%

A knowledge‐based approach to Information Extraction for semantic interoperability in the archaeology domain

Vlachidis

Tudhope

2015

Asso for Info Science & Tech

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The article presents a method for automatic semantic indexing of archaeological grey‐literature reports using empirical (rule‐based) Information Extraction techniques in combination with domain‐specific knowledge organization systems. The semantic annotation system (OPTIMA) performs the tasks of Named Entity Recognition, Relation Extraction, Negation Detection, and Word‐Sense Disambiguation using hand‐crafted rules and terminological resources for associating contextual abstractions with classes of the standard ontology CIDOC Conceptual Reference Model (CRM) for cultural heritage and its archaeological extension, CRM‐EH. Relation Extraction (RE) performance benefits from a syntactic‐based definition of RE patterns derived from domain oriented corpus analysis. The evaluation also shows clear benefit in the use of assistive natural language processing (NLP) modules relating to Word‐Sense Disambiguation, Negation Detection, and Noun Phrase Validation, together with controlled thesaurus expansion. The semantic indexing results demonstrate the capacity of rule‐based Information Extraction techniques to deliver interoperable semantic abstractions (semantic annotations) with respect to the CIDOC CRM and archaeological thesauri. Major contributions include recognition of relevant entities using shallow parsing NLP techniques driven by a complimentary use of ontological and terminological domain resources and empirical derivation of context‐driven RE rules for the recognition of semantic relationships from phrases of unstructured text.

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Section: Optima Pipelinementioning

confidence: 99%

A knowledge‐based approach to Information Extraction for semantic interoperability in the archaeology domain

Vlachidis

Tudhope

2015

Asso for Info Science & Tech

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“…The OPTIMA NER phase also implements a negation detection mechanism targeted at matching phrases which negate any of the four CRM entities (Place, Physical Object, Material and Time Appellation) (Vlachidis and Tudhope 2013). The implemented mechanism enhances the NegEx algorithm (Chapman et al 2001) addressing known limitations and domain related issues.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Archaeological Discriminant Applications of the Lubischew Test

Guerrero¹,

Serrano²,

Jiménez‐Arenas³

2015

Mathematics and Archaeology

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IntroductionArchaeologists generate large quantities of text, ranging from unpublished technical fieldwork reports (the 'grey literature') to synthetic journal articles. However, the indexing and analysis of these documents can be time consuming and lacks consistency when done by hand. It is also rarely integrated with the wider archaeological information domain, with bibliographic searches having to be undertaken independently of database queries, for example. Text mining offers a means of extracting information from large volumes of text, providing researchers with an easy way of locating relevant texts and also of identifying patterns in the literature. In recent years techniques of Natural Language Processing (NLP) and its subfield, Information Extraction (IE), have been adopted to allow researchers to find, compare and analyse relevant documents, and to link them to other types of data. This chapter introduces the underpinning mathematics and provides a short presentation of the algorithms and distance measures used, from the point of view of artificial intelligence and computational logic. It describes the different NLP schools of thought and compares the pros and cons of rule-based vs machine learning approaches to information extraction. The role of ontologies and named entity recognition will be discussed and the chapter demonstrates how IE can provide the basis for semantic annotation and how it contributes to the construction of a semantic web for archaeology. The authors have worked on a number of projects that have employed techniques from NLP and IE in Archaeology, including Archaeotools, STAR and STELLAR. The chapter describes the archaeological user needs requirement, drawing examples from several countries, and the authors present examples drawn from their own projects, and previous work by others, of how NLP and IE can contribute to addressing this need. The problems and challenges of employing text mining in the archaeological domain are discussed, as well as the potential benefits.

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“…The OPTIMA NER phase also implements a negation detection mechanism targeted at matching phrases which negate any of the four CRM entities (Vlachidis and Tudhope 2013). The implemented mechanism enhances the NegEx algorithm (Chapman et al 2001) addressing known limitations and domain related issues.…”

Section: Named Entity Recognitionmentioning

confidence: 99%