Machine-readable dictionary

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Machine-readable dictionary (MRD) is a dictionary stored as machine (computer) data instead of being printed on paper. It is an electronic dictionary and lexical database.

A machine-readable dictionary is a dictionary in an electronic form that can be loaded in a database and can be queried via application software. It may be a single language explanatory dictionary or a multi-language dictionary to support translations between two or more languages or a combination of both. Translation software between multiple languages usually apply bidirectional dictionaries. An MRD may be a dictionary with a proprietary structure that is queried by dedicated software (for example online via internet) or it can be a dictionary that has an open structure and is available for loading in computer databases and thus can be used via various software applications. Conventional dictionaries contain a lemma with various descriptions. A machine-readable dictionary may have additional capabilities and is therefore sometimes called a smart dictionary. An example of a smart dictionary is the Open Source Gellish English dictionary.
The term dictionary is also used to refer to an electronic vocabulary or lexicon as used for example in spelling checkers. If dictionaries are arranged in a subtype-supertype hierarchy of concepts (or terms) then it is called a taxonomy. If it also contains other relations between the concepts, then it is called an ontology. Search engines may use either a vocabulary, a taxonomy or an ontology to optimise the search results. Specialised electronic dictionaries are morphological dictionaries or syntactic dictionaries.
The term MRD is often contrasted with NLP dictionary, in the sense that an MRD is the electronic form of a dictionary which was printed before on paper. Although being both used by programs, in contrast, the term NLP dictionary is preferred when the dictionary was built from scratch with NLP in mind. An ISO standard for MRD and NLP is able to represent both structures and is called Lexical Markup Framework. [1]

History

The first widely distributed MRDs were the Merriam-Webster Seventh Collegiate (W7) and the Merriam-Webster New Pocket Dictionary (MPD). Both were produced by a government-funded project at System Development Corporation under the direction of John Olney. They were manually keyboarded as no typesetting tapes of either book were available. Originally each was distributed on multiple reels of magnetic tape as card images with each separate word of each definition on a separate punch card with numerous special codes indicating the details of its usage in the printed dictionary. Olney outlined a grand plan for the analysis of the definitions in the dictionary, but his project expired before the analysis could be carried out. Robert Amsler at the University of Texas at Austin resumed the analysis and completed a taxonomic description of the Pocket Dictionary under National Science Foundation funding, however his project expired before the taxonomic data could be distributed. Roy Byrd et al. at IBM Yorktown Heights resumed analysis of the Webster's Seventh Collegiate following Amsler's work. Finally, in the 1980s starting with initial support from Bellcore and later funded by various U.S. federal agencies, including NSF, ARDA, DARPA, DTO, and REFLEX, George Armitage Miller and Christiane Fellbaum at Princeton University completed the creation and wide distribution of a dictionary and its taxonomy in the WordNet project, which today stands as the most widely distributed computational lexicology resource.

Related Research Articles

The Semantic Web is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

WordNet Computational lexicon of English

WordNet is a lexical database of semantic relations between words in more than 200 languages. WordNet links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. WordNet can thus be seen as a combination and extension of a dictionary and thesaurus. While it is accessible to human users via a web browser, its primary use is in automatic text analysis and artificial intelligence applications. WordNet was first created in the English language and the English WordNet database and software tools have been released under a BSD style license and are freely available for download from that WordNet website.

Wiktionary Multilingual, web-based project to create a free content dictionary

Wiktionary is a multilingual, web-based project to create a free content dictionary of terms in all natural languages and in a number of artificial languages. These entries may contain definitions, images for illustrations, pronunciations, etymologies, inflections, usage examples, quotations, related terms, and translations of words into other languages, among other features. It is collaboratively edited via a wiki. Its name is a portmanteau of the words wiki and dictionary. It is available in 171 languages and in Simple English. Like its sister project Wikipedia, Wiktionary is run by the Wikimedia Foundation, and is written collaboratively by volunteers, dubbed "Wiktionarians". Its wiki software, MediaWiki, allows almost anyone with access to the website to create and edit entries.

A monolingual learner's dictionary (MLD) is designed to meet the reference needs of people learning a foreign language. MLDs are based on the premise that language-learners should progress from a bilingual dictionary to a monolingual one as they become more proficient in their target language, but that general-purpose dictionaries are inappropriate for their needs. Dictionaries for learners include information on grammar, usage, common errors, collocation, and pragmatics, which is largely missing from standard dictionaries, because native speakers tend to know these aspects of language intuitively. And while the definitions in standard dictionaries are often written in difficult language, those in an MLD use a simple and accessible defining vocabulary.

A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure.

Lexical may refer to:

Electronic dictionary Dictionary whose data exists in digital form and can be accessed through a number of different media

An electronic dictionary is a dictionary whose data exists in digital form and can be accessed through a number of different media. Electronic dictionaries can be found in several forms, including software installed on tablet or desktop computers, mobile apps, web applications, and as a built-in function of E-readers. They may be free or require payment.

Bilingual dictionary Specialized dictionary used to translate words or phrases from one language to another

A bilingual dictionary or translation dictionary is a specialized dictionary used to translate words or phrases from one language to another. Bilingual dictionaries can be unidirectional, meaning that they list the meanings of words of one language in another, or can be bidirectional, allowing translation to and from both languages. Bidirectional bilingual dictionaries usually consist of two sections, each listing words and phrases of one language alphabetically along with their translation. In addition to the translation, a bilingual dictionary usually indicates the part of speech, gender, verb type, declension model and other grammatical clues to help a non-native speaker use the word. Other features sometimes present in bilingual dictionaries are lists of phrases, usage and style guides, verb tables, maps and grammar references. In contrast to the bilingual dictionary, a monolingual dictionary defines words and phrases instead of translating them.

Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s. It started out as an engineering modeling language but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.

Linguistic categories include

Computational lexicology is a branch of computational linguistics, which is concerned with the use of computers in the study of lexicon. It has been more narrowly described by some scholars as the use of computers in the study of machine-readable dictionaries. It is distinguished from computational lexicography, which more properly would be the use of computers in the construction of dictionaries, though some researchers have used computational lexicography as synonymous.

Language resource management - Lexical markup framework, is the ISO International Organization for Standardization ISO/TC37 standard for natural language processing (NLP) and machine-readable dictionary (MRD) lexicons. The scope is standardization of principles and methods relating to language resources in the contexts of multilingual communication.

In digital lexicography, natural language processing, and digital humanities, a lexical resource is a language resource consisting of data regarding the lexemes of the lexicon of one or more languages e.g., in the form of a database.

Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natural languages and natural-language user interfaces include Inform 7, a natural programming language for making interactive fiction, Shakespeare, an esoteric natural programming language in the style of the plays of William Shakespeare, and Wolfram Alpha, a computational knowledge engine, using natural-language input. Some methods for program synthesis are based on natural-language programming.

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.

The following outline is provided as an overview of and topical guide to natural language processing:

UBY is a large-scale lexical-semantic resource for natural language processing (NLP) developed at the Ubiquitous Knowledge Processing Lab (UKP) in the department of Computer Science of the Technische Universität Darmstadt . UBY is based on the ISO standard Lexical Markup Framework (LMF) and combines information from several expert-constructed and collaboratively constructed resources for English and German.

In natural language processing, linguistics, and neighboring fields, Linguistic Linked Open Data (LLOD) describes a method and an interdisciplinary community concerned with creating, sharing, and (re-)using language resources in accordance with Linked Data principles. The Linguistic Linked Open Data Cloud was conceived and is being maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation, but has been a point of focal activity for several W3C community groups, research projects, and infrastructure efforts since then.

OntoLex is the short name of a vocabulary for lexical resources in the web of data (OntoLex-Lemon) and the short name of the W3C community group that created it.

References

  1. Gil Francopoulo (edited by) LMF Lexical Markup Framework, ISTE / Wiley 2013 ( ISBN   978-1-84821-430-9)