ORBi: Gohy Stéphanie - Automated text categorization in a dead language. The detection of genres in Late Egyptian

Reference : Automated text categorization in a dead language. The detection of genres in Late Egyptian


	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Arts & humanities : Languages & linguistics Arts & humanities : Classical & oriental studies Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/110298

Title :	Automated text categorization in a dead language. The detection of genres in Late Egyptian
Language :	English
Author, co-author :	Gohy, Stéphanie [Université de Liège - ULg > Département des sciences de l'antiquité > Egyptologie >]
	Martin Leon, Benjamin [Université de Liège - ULg > Département des sciences de l'antiquité > Egyptologie >]
	Polis, Stéphane [Université de Liège - ULg > Département des sciences de l'antiquité > Egyptologie >]
Publication date :	2013
Main document title :	Texts, Languages & Information Technology in Egyptology. Selected papers from the meeting of the Computer Working Group of the International Association of Egyptologists (Informatique & Égyptologie), Liège, 6-8 July 2010
Editor :	Polis, Stéphane
	Winand, Jean
Publisher :	Presses Universitaires de Liège
Collection and collection volume :	Aegyptiaca Leodiensia 9
Pages :	61-74
Peer reviewed :	Yes
On invitation :	Yes
Audience :	International
City :	Liège
Country :	Belgium
Event name :	Informatique & Égyptologie 2010. Texts, Languages & Information Technology in Egyptology
Event date :	6-8 juillet 2010
Event organizer :	Stéphane Polis & Jean Winand
Event place (city) :	Liège
Event country :	Belgium
Abstract :	[en] This paper is a first step in applying machine learning methods typical of Automated Text Catego-rization (ATC) for Automatic Genre Identification (AGI) in Late Egyptian, a language written in either hieroglyphic or hieratic scripts that is found in documents from Ancient Egypt dating from ca. 1350-700 BCE. The study is divided into three parts. After a general intro¬duction on AGI (§1), we introduce the levels of annotation that are integrated in the Ramses corpus and can be used when performing AGI on Late Egyptian (§2). In the following section (§3) we offer a brief survey of the types of features that have been discussed in the literature on AGI, before proceeding with three case studies where we apply supervised machine learning methods — namely the naïve Bayes classifier (§4.1), the Support Vector Machine (§4.2), and the Segment and Combine approach (§4.3) — to a selection of texts in the corpus. Their respective performances are tested using lexical, part-of-speech and inflectional features.
Funders :	Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Target :	Researchers
Permalink :	http://hdl.handle.net/2268/110298

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	File	Commentary	Version	Size	Access
Open access	AegLeod9_04_Ramses3.pdf		Publisher postprint	2.92 MB	View/Open

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