Model Description

Francopoulo, Gil; George, Monte

doi:10.1002/9781118712696.ch2

Cited by 7 publications

(11 citation statements)

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“…LMF, designed between 2003 and 2008, is ISO standard 24613:2008 and it provides a standardised framework for natural language processing (NLP) and machine-readable dictionaries [31]. While it is able to represent linguistic information, it cannot represent lexicons to ontologies [32], [33] (p. 3), and although it describes itself as interoperable, LMF has been criticised for its inability to establish interoperability between different lexicons, as well as its vagueness for use when applied to different [34] (p. 96).…”

Section: Step 2: Select the Model With Which To Describe The Linguistmentioning

confidence: 99%

“…Considering the use cases identified in Step 1, the requirements of the model are broadly defined in sections based on those identified by Cimiano et al [35] (pp. [29][30][31][32][33], where the model should allow for:…”

Section: Step 2: Select the Model With Which To Describe The Linguistmentioning

confidence: 99%

“…A lemma is the address of a lexical entry which is used to retrieve information; a word, a word stem, or a multiword expression can all be lemmas in the same lemma list [31] (pp. [64][65][66][67].…”

Section: Step 7: Consider the Lemmatisation Approachmentioning

confidence: 99%

“…[64][65][66][67]. The lexicographic tradition for the lemmatisation of nouns and verbs, namely word versus stem, will vary depending on the conjunctiveness or disjunctiveness of the orthography of the language concerned [31] (p. 68). Due to this variation, the lemmatisation approach for isiXhosa, an agglutinative language, had to be considered when constructing the LLDF [19] (pp.…”

Section: Step 7: Consider the Lemmatisation Approachmentioning

confidence: 99%

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Conversion of the English-Xhosa Dictionary for Nurses to a Linguistic Linked Data Framework

Gillis-Webber

2018

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The English-Xhosa Dictionary for Nurses (EXDN) is a bilingual, unidirectional printed dictionary in the public domain, with English and isiXhosa as the language pair. By extending the digitisation efforts of EXDN from a human-readable digital object to a machine-readable state, using Resource Description Framework (RDF) as the data model, semantically interoperable structured data can be created, thus enabling EXDN's data to be reused, aggregated and integrated with other language resources, where it can serve as a potential aid in the development of future language resources for isiXhosa, an under-resourced language in South Africa. The methodological guidelines for the construction of a Linguistic Linked Data framework (LLDF) for a lexicographic resource, as applied to EXDN, are described, where an LLDF can be defined as a framework: (1) which describes data in RDF, (2) using a model designed for the representation of linguistic information, (3) which adheres to Linked Data principles, and (4) which supports versioning, allowing for change. The result is a bidirectional lexicographic resource, previously bounded and static, now unbounded and evolving, with the ability to extend to multilingualism.Information 2018, 9, 274 2 of 30 into a simple digital resource, typically as images in JPEG format. However, by retrodigitising the artefact, converting it from a simple digital resource to a complex digital resource, it has the potential to become machine-interoperable. The form of complex digital objects can vary, with examples including: a collection of XML files; the same content but as HTML files with semantic markup to describe the content therein; as a dataset stored in a relational database or as Resource Description Framework (RDF). For the latter example, RDF "is a framework for representing information in the Web", published by the World Wide Web Consortium (W3C), where data is described in short statements, called triples, which are of the form subject-predicate-object [9]. A subject can be an internationalised resource identifier (IRI) or a blank node; an object can be an IRI, blank node, or a literal; and a predicate can be an IRI only [9].The lexical entry Abdomen can be expressed in the following short statements:Abdomen is a lexical entry.Abdomen is a word.Abdomen is a noun.Abdomen is an English term.Using Turtle syntax, a human-readable serialisation of RDF, the same short statements can be described in RDF: SubjectPredicate Object :abdomen :isA :lexicalEntry ; :isA :word ; :isA :noun ; :isLanguage :English .These short statements are visualised in Figure 1, using node-edge-node structure. Information 2018, 9, x FOR PEER REVIEW 2 of 29Despite EXDN being published more than seventy-five years ago, as a language resource for an under-resourced indigenous African language, its content is still of value [1] (p. 1). EXDN is in print form, bounded and static, and by digitising the dictionary, it is converted from an analogue resource into a simple digital resource, typically as images in JPEG format. Howev...

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Section: Step 2: Select the Model With Which To Describe The Linguistmentioning

confidence: 99%

Section: Step 2: Select the Model With Which To Describe The Linguistmentioning

confidence: 99%

Section: Step 7: Consider the Lemmatisation Approachmentioning

confidence: 99%

Section: Step 7: Consider the Lemmatisation Approachmentioning

confidence: 99%

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Conversion of the English-Xhosa Dictionary for Nurses to a Linguistic Linked Data Framework

Gillis-Webber

2018

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“…To meet this challenge, several projects were conducted such as ACQUILEX (Bogurev et al 1988), GENELEX (Antoni-Lay, Francopoulo and Zaysser 1994), TEI (Véronis and Ide 1996), Eagles (Calzolari, Mc Naught and Zampolli 1996), Multext (Calzolari and Monachini 1996), SIMPLE (Lenci et al 2000) and ISLE (Bertagna et al 2000). 2 These projects led to the emergence of the LMF (Lexical Markup Framework) ISO standard for the lexical structure modeling (ISO 24613) (Francopoulo 2003), (Francopoulo and George 2008) in association with the ISO Data Categories Registry (DCR) 3 following ISO 12620 (Ide and Romary 2004). These standards were designed by a group of sixty ISO experts coming from different cultures, languages and continents.…”

Section: Introductionmentioning

confidence: 99%

ISO standard modeling of a large Arabic dictionary

et al. 2015

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In this paper, we address the problem of the large coverage dictionaries of Arabic language usable both for direct human reading and automatic Natural Language Processing. For these purposes, we propose a normalized and implemented modeling, based on Lexical Markup Framework (LMF-ISO 24613) and Data Registry Category (DCR-ISO 12620), which allows a stable and well-defined interoperability of lexical resources through a unification of the linguistic concepts. Starting from the features of the Arabic language, and due to the fact that a large range of details and refinements need to be described specifically for Arabic, we follow a finely structuring strategy. Besides its richness in morphology, syntax and semantics knowledge, our model includes all the Arabic morphological patterns to generate the inflected forms from a given lemma and highlights the syntactic-semantic relations. In addition, an appropriate codification has been designed for the management of all types of relationships among lexical entries and their related knowledge. According to this model, a dictionary named El Madar 1 has been built and is now publicly available on line. The data are managed by a user-friendly Web-based lexicographical workstation. This work has not been done in isolation, but is the result of a collaborative effort by an international team mainly within the ISO network during a period of eight years. A. Khemakhem et al.is to merge them in order to obtain a new richer resource. More generally, the exchange remains a difficult (and expensive) issue when nothing has been scheduled for this purpose. To meet this challenge, several projects were conducted such as ACQUILEX (Bogurev et al. These projects led to the emergence of the LMF (Lexical Markup Framework) ISO standard for the lexical structure modeling (ISO 24613) (Francopoulo 2003), (Francopoulo and George 2008) in association with the ISO Data Categories Registry (DCR) 3 following ISO 12620 (Ide and Romary 2004). These standards were designed by a group of sixty ISO experts coming from different cultures, languages and continents. Numerous developments followed in different parts of the world. 4 Unfortunately, the Arabic language did not immediately benefit from the emergence of these standards, although it is spoken by more than 300 million people around the world, and is the official language of more than twenty countries. The language still uses references to different printed dictionaries based on incompatible lexicographical schools. Only few works tried the application of LMF on the Arabic language out, according to previous revisions of this standard. Some developments were made in morphology (Khemakhem, Gargouri and Abdelwahed 2006), (Romary, Salmon-Alt and Francopoulo 2004), (Salmon-Alt, Akrout and Romary 2005) and some studies were conducted in syntax (Loukil, Haddar and Ben Hamadou 2008). However, these works were developed during the drafting of the LMF standard and were not updated according to the ISO validation.Obviously, the situation of the Arabic lexica...

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Lexical data mining‐based approach for the self‐enrichment ofLMFstandardized dictionaries: Case of the syntactico‐semantic knowledge

Elleuch

Gargouri

Hamadou

2021

Concurrency and Computation

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Summary The LMF ISO standard provides a large cover of lexical knowledge using a fine structure. However, like most of the electronic dictionaries, the available normalized LMF dictionaries comprise only basic morpho‐syntactic and semantic knowledge, such as the meanings of lexical entries through the definitions and the associated examples, and sometimes the indication of the synonyms and antonyms. Other sophisticated knowledge, such as the syntactic behaviors, semantic classes and syntactico‐semantic links, which are scarce, requires a high expertise and its adding to dictionaries is expensive. In fact in this paper, we propose an approach of lexical data mining of the widely available textual content associated with the meanings, notably in the normalized LMF dictionaries, in order to perform the self‐enrichment of these dictionaries. First, we contribute to the enrichment of the syntactic behaviors by linking them to the suitable meanings. Second, we focus on the enrichment of the meanings of LMF lexical entries with semantic classes based on the Gaston Gross semantic classification. Finally, we establish the syntactico‐semantic links based on the results of the syntactic and semantic enrichment processes. The proposed approach has been consolidated by an experimentation carried out on an available normalized LMF dictionary for Arabic language.

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Model Description

Cited by 7 publications

References 0 publications

Conversion of the English-Xhosa Dictionary for Nurses to a Linguistic Linked Data Framework

Conversion of the English-Xhosa Dictionary for Nurses to a Linguistic Linked Data Framework

ISO standard modeling of a large Arabic dictionary

Lexical data mining‐based approach for the self‐enrichment ofLMFstandardized dictionaries: Case of the syntactico‐semantic knowledge

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