Problem of Creating a Professional Dictionary of Uncodified Vocabulary
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Methods in Lexicography and Dictionary Research* Stefan J
Methods in Lexicography and Dictionary Research* Stefan J. Schierholz, Department Germanistik und Komparatistik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany ([email protected]) Abstract: Methods are used in every stage of dictionary-making and in every scientific analysis which is carried out in the field of dictionary research. This article presents some general consid- erations on methods in philosophy of science, gives an overview of many methods used in linguis- tics, in lexicography, dictionary research as well as of the areas these methods are applied in. Keywords: SCIENTIFIC METHODS, LEXICOGRAPHICAL METHODS, THEORY, META- LEXICOGRAPHY, DICTIONARY RESEARCH, PRACTICAL LEXICOGRAPHY, LEXICO- GRAPHICAL PROCESS, SYSTEMATIC DICTIONARY RESEARCH, CRITICAL DICTIONARY RESEARCH, HISTORICAL DICTIONARY RESEARCH, RESEARCH ON DICTIONARY USE Opsomming: Metodes in leksikografie en woordeboeknavorsing. Metodes word gebruik in elke fase van woordeboekmaak en in elke wetenskaplike analise wat in die woor- deboeknavorsingsveld uitgevoer word. In hierdie artikel word algemene oorwegings vir metodes in wetenskapfilosofie voorgelê, 'n oorsig word gegee van baie metodes wat in die taalkunde, leksi- kografie en woordeboeknavorsing gebruik word asook van die areas waarin hierdie metodes toe- gepas word. Sleutelwoorde: WETENSKAPLIKE METODES, LEKSIKOGRAFIESE METODES, TEORIE, METALEKSIKOGRAFIE, WOORDEBOEKNAVORSING, PRAKTIESE LEKSIKOGRAFIE, LEKSI- KOGRAFIESE PROSES, SISTEMATIESE WOORDEBOEKNAVORSING, KRITIESE WOORDE- BOEKNAVORSING, HISTORIESE WOORDEBOEKNAVORSING, NAVORSING OP WOORDE- BOEKGEBRUIK 1. Introduction In dictionary production and in scientific work which is carried out in the field of dictionary research, methods are used to reach certain results. Currently there is no comprehensive and up-to-date documentation of these particular methods in English. The article of Mann and Schierholz published in Lexico- * This article is based on the article from Mann and Schierholz published in Lexicographica 30 (2014). -
Organizing Knowledge: Comparative Structures of Intersubjectivity in Nineteenth-Century Historical Dictionaries
Organizing Knowledge: Comparative Structures of Intersubjectivity in Nineteenth-Century Historical Dictionaries Kelly M. Kistner A dissertation submitted in partial fulfillment for the requirements for the degree of Doctor of Philosophy University of Washington 2014 Reading Committee: Gary G. Hamilton, Chair Steven Pfaff Katherine Stovel Program Authorized to Offer Degree: Sociology ©Copyright 2014 Kelly M. Kistner University of Washington Abstract Organizing Knowledge: Comparative Structures of Intersubjectivity in Nineteenth-Century Historical Dictionaries Kelly Kistner Chair of the Supervisory Committee: Professor Gary G. Hamilton Sociology Between 1838 and 1857 language scholars throughout Europe were inspired to create a new kind of dictionary. Deemed historical dictionaries, their projects took an unprecedented leap in style and scale from earlier forms of lexicography. These lexicographers each sought to compile historical inventories of their national languages and were inspired by the new scientific approach of comparative philology. For them, this science promised a means to illuminate general processes of social change and variation, as well as the linguistic foundations for cultural and national unity. This study examines two such projects: The German Dictionary, Deutsches Worterbuch, of the Grimm Brothers, and what became the Oxford English Dictionary. Both works utilized collaborative models of large-scale, long-term production, yet the content of the dictionaries would differ in remarkable ways. The German dictionary would be characterized by its lack of definitions of meaning, its eclectic treatment of entries, rich analytical prose, and self- referential discourse; whereas the English dictionary would feature succinct, standardized, and impersonal entries. Using primary source materials, this research investigates why the dictionaries came to differ. -
Historical Astrolexicography and Old Publications
Library and Information Services in Astronomy III ASP Conference Series, Vol. 153, 1998 U. Grothkopf, H. Andernach, S. Stevens-Rayburn, and M. Gomez (eds.) Historical Astrolexicography and Old Publications Terry J. Mahoney Instituto de Astrof´ısica de Canarias, E-38200 La Laguna, Tenerife, Spain Abstract. I describe how the principles of lexicography have been ap- plied in limited ways in astronomy and look at the revision work under way for the third edition of the Oxford English Dictionary,which,when completed, will contain the widest and most detailed coverage of the as- tronomical lexicon in the English language. Finally, I argue the need for a dedicated historical dictionary of astronomy based rigorously on a corpus of quotations from sources published in English from the beginnings of written English to the present day. 1. The Role of Lexicography in Astronomy Lexicography is defined in the Oxford English Dictionary1 as “the writing or com- pilation of a lexicon or dictionary.” Among the various senses for lexicon listed in OED2 is, “The vocabulary proper to some department of knowledge or sphere of activity.” I define astrolexicography as the application of the practices of lex- icography to the astronomical lexicon. However, the discipline of lexicography may be applied to many types of reference works other than dictionaries (for example, “encyclopaedic dictionaries”, glossaries of technical terms, thesauri, lists of key words, etc.). A full survey of astronomical lexicographical works will be the subject of a future paper. Here I shall limit myself to identifying what I consider to be an important lacuna in astronomical literature. -
Towards a Conceptual Representation of Lexical Meaning in Wordnet
Towards a Conceptual Representation of Lexical Meaning in WordNet Jen-nan Chen Sue J. Ker Department of Information Management, Department of Computer Science, Ming Chuan University Soochow University Taipei, Taiwan Taipei, Taiwan [email protected] [email protected] Abstract Knowledge acquisition is an essential and intractable task for almost every natural language processing study. To date, corpus approach is a primary means for acquiring lexical-level semantic knowledge, but it has the problem of knowledge insufficiency when training on various kinds of corpora. This paper is concerned with the issue of how to acquire and represent conceptual knowledge explicitly from lexical definitions and their semantic network within a machine-readable dictionary. Some information retrieval techniques are applied to link between lexical senses in WordNet and conceptual ones in Roget's categories. Our experimental results report an overall accuracy of 85.25% (87, 78, 89, and 87% for nouns, verbs, adjectives and adverbs, respectively) when evaluating on polysemous words discussed in previous literature. 1 Introduction Knowledge representation has traditionally been thought of as the heart of various applications of natural language processing. Anyone who has built a natural language processing system has had to tackle the problem of representing its knowledge of the world. One of the applications for knowledge representation is word sense disambiguation (WSD) for unrestricted text, which is one of the major problems in analyzing sentences. In the past, the substantial literature on WSD has concentrated on statistical approaches to analyzing and extracting information from corpora (Gale et al. 1992; Yarowsky 1992, 1995; Dagan and Itai 1994; Luk 1995; Ng and Lee 1996). -
Inferring Parts of Speech for Lexical Mappings Via the Cyc KB
to appear in Proceeding 20th International Conference on Computational Linguistics (COLING-04), August 23-27, 2004 Geneva Inferring parts of speech for lexical mappings via the Cyc KB Tom O’Hara‡, Stefano Bertolo, Michael Witbrock, Bjørn Aldag, Jon Curtis,withKathy Panton, Dave Schneider,andNancy Salay ‡Computer Science Department Cycorp, Inc. New Mexico State University Austin, TX 78731 Las Cruces, NM 88001 {bertolo,witbrock,aldag}@cyc.com [email protected] {jonc,panton,daves,nancy}@cyc.com Abstract in the phrase “painting for sale.” In cases like this, semantic or pragmatic criteria, as opposed We present an automatic approach to learn- to syntactic criteria only, are necessary for deter- ing criteria for classifying the parts-of-speech mining the proper part of speech. The headword used in lexical mappings. This will fur- part of speech is important for correctly identifying ther automate our knowledge acquisition sys- phrasal variations. For instance, the first term can tem for non-technical users. The criteria for also occur in the same sense in “paint for money.” the speech parts are based on the types of However, this does not hold for the second case, the denoted terms along with morphological since “paint for sale” has an entirely different sense and corpus-based clues. Associations among (i.e., a substance rather than an artifact). these and the parts-of-speech are learned us- When lexical mappings are produced by naive ing the lexical mappings contained in the Cyc users, such as in DARPA’s Rapid Knowledge For- knowledge base as training data. With over mation (RKF) project, it is desirable that tech- 30 speech parts to choose from, the classifier nical details such as the headword part of speech achieves good results (77.8% correct). -
Symbols Used in the Dictionary
Introduction 1. This dictionary will help you to be able to use Einglish - English Dictionary well & use it in your studying. 2. The exercies will provide you with what information your dictionary contains & how to find as well use them quickly & correctly. So you MUST: 1. make sure you understand the instructuion of the exercise. 2. check on yourself by working out examples, using your dictionay. 3. Do it carefully but quickly as you can even if you think you know the answer. 4. always scan the whole entry first to get the general idea, then find the part that you’re interested in. 5. Read slowly & carefully. 6. Help to make the learning process interesting. 7. If you make a mistake, find out why. Remember, the more you use the dictionary, the more uses you will find for it. 1 Chapter one Dictionary Entry • Terms used in the Dictionary. •Symbols used in the Dictionary. •Different typfaces used in the Dictionary. •Abbreviation used in the Dictionary. 2 1- Terms used in the Dictionay: -What is Headword? -What is an Entry? -What is a Definition? -What is a Compound? -What is a Derivative? -What information does an entry contain? 3 1- What is Headword? It’s the word you look up in a dictionary. Also, it’s the name given to each of the words listed alphabetically. 4 2- What is an Entry? It consists of a headword & all the information about the headword. 5 3- What is a Definition? It’s a part of an entry which describes a particular meaning & usage of the headword. -
DICTIONARY News
Number 17 y July 2009 Kernerman kdictionaries.com/kdn DICTIONARY News KD’s BLDS: a brief introduction In 2005 K Dictionaries (KD) entered a project of developing We started by establishing an extensive infrastructure both dictionaries for learners of different languages. KD had already contentwise and technologically. The lexicographic compilation created several non-English titles a few years earlier, but those was divided into 25 projects: 1 for the French core, 8 for the were basic word-to-word dictionaries. The current task marked dictionary cores of the eight languages, another 8 for the a major policy shift for our company since for the first time we translation from French to eight languages, and 8 more for the were becoming heavily involved in learner dictionaries with translation of each language to French. target languages other than English. That was the beginning of An editorial team was set up for developing each of the nine our Bilingual Learners Dictionaries Series (BLDS), which so (French + 8) dictionary cores. The lexicographers worked from far covers 20 different language cores and keeps growing. a distance, usually at home, all over the world. The chief editor The BLDS was launched with a program for eight French for each language was responsible for preparing the editorial bilingual dictionaries together with Assimil, a leading publisher styleguide and the list of headwords. Since no corpora were for foreign language learning in France which made a strategic publicly available for any of these languages, each editor used decision to expand to dictionaries in cooperation with KD. different means to retrieve information in order to compile the The main target users were identified as speakers of French headword list. -
Japanese-English Cross-Language Headword Search of Wikipedia
Japanese-English Cross-Language Headword Search of Wikipedia Satoshi Sato and Masaya Okada Graduate School of Engineering Nagoya University Chikusa-ku, Nagoya, Japan [email protected] masaya [email protected] Abstract glish article. To realize CLHS, term translation is required. This paper describes a Japanese-English cross-language headword search system of Term translation is not a mainstream of machine Wikipedia, which enables to find an appro- translation research, because a simple dictionary- priate English article from a given Japanese based method is widely used and enough for query term. The key component of the sys- general terms. For technical terms and proper tem is a term translator, which selects an ap- nouns, automatic extraction of translation pairs propriate English headword among the set from bilingual corpora and the Web has been in- of headwords in English Wikipedia, based on the framework of non-productive ma- tensively studied (e.g., (Daille et al., 1994) and chine translation. An experimental result (Lin et al., 2008)) in order to fill the lack of en- shows that the translation performance of tries in a bilingual dictionary. However, the qual- our system is equal or slightly better than ity of automatic term translation is not well exam- commercial machine translation systems, ined with a few exception (Baldwin and Tanaka, Google translate and Mac-Transer. 2004). Query translation in cross-language infor- mation retrieval is related to term translation, but it concentrates on translation of content words in 1 Introduction a query (Fujii and Ishikawa, 2001). Many people use Wikipedia, an encyclopedia on In the CLHS situation, the ultimate goal of the Web, to find meanings of unknown terms. -
The Art of Lexicography - Niladri Sekhar Dash
LINGUISTICS - The Art of Lexicography - Niladri Sekhar Dash THE ART OF LEXICOGRAPHY Niladri Sekhar Dash Linguistic Research Unit, Indian Statistical Institute, Kolkata, India Keywords: Lexicology, linguistics, grammar, encyclopedia, normative, reference, history, etymology, learner’s dictionary, electronic dictionary, planning, data collection, lexical extraction, lexical item, lexical selection, typology, headword, spelling, pronunciation, etymology, morphology, meaning, illustration, example, citation Contents 1. Introduction 2. Definition 3. The History of Lexicography 4. Lexicography and Allied Fields 4.1. Lexicology and Lexicography 4.2. Linguistics and Lexicography 4.3. Grammar and Lexicography 4.4. Encyclopedia and lexicography 5. Typological Classification of Dictionary 5.1. General Dictionary 5.2. Normative Dictionary 5.3. Referential or Descriptive Dictionary 5.4. Historical Dictionary 5.5. Etymological Dictionary 5.6. Dictionary of Loanwords 5.7. Encyclopedic Dictionary 5.8. Learner's Dictionary 5.9. Monolingual Dictionary 5.10. Special Dictionaries 6. Electronic Dictionary 7. Tasks for Dictionary Making 7.1. Panning 7.2. Data Collection 7.3. Extraction of lexical items 7.4. SelectionUNESCO of Lexical Items – EOLSS 7.5. Mode of Lexical Selection 8. Dictionary Making: General Dictionary 8.1. HeadwordsSAMPLE CHAPTERS 8.2. Spelling 8.3. Pronunciation 8.4. Etymology 8.5. Morphology and Grammar 8.6. Meaning 8.7. Illustrative Examples and Citations 9. Conclusion Acknowledgements ©Encyclopedia of Life Support Systems (EOLSS) LINGUISTICS - The Art of Lexicography - Niladri Sekhar Dash Glossary Bibliography Biographical Sketch Summary The art of dictionary making is as old as the field of linguistics. People started to cultivate this field from the very early age of our civilization, probably seven to eight hundred years before the Christian era. -
Download in the Conll-Format and Comprise Over ±175,000 Tokens
Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning Mike Kestemont1, Jeroen De Gussem2 1 University of Antwerp, Belgium 2 Ghent University, Belgium *Corresponding author: [email protected] Abstract In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is typical of medieval Latin. In Digital Classics, these tasks are traditionally solved in a (i) cascaded and (ii) lexicon-dependent fashion. For example, a lexicon is used to generate all the potential lemma-tag pairs for a token, and next, a context-aware PoS-tagger is used to select the most appropriate tag-lemma pair. Apart from the problems with out-of-lexicon items, error percolation is a major downside of such approaches. In this paper we explore the possibility to elegantly solve these tasks using a single, integrated approach. For this, we make use of a layered neural network architecture from the field of deep representation learning. keywords tagging, lemmatization, medieval Latin, LSTM, deep learning INTRODUCTION Latin —and its historic variants in particular— have long been a topic of major interest in Natural Language Processing [Piotrowksi 2012]. Especially in the community of Digital Humanities, the automated processing of Latin texts has always been a popular research topic. In a variety of computational applications, such as text re-use detection [Franzini et al, 2015], it is desirable to annotate and augment Latin texts with useful morpho-syntactical or lexical information, such as lemmas. -
Phonemic Similarity Metrics to Compare Pronunciation Methods
Phonemic Similarity Metrics to Compare Pronunciation Methods Ben Hixon1, Eric Schneider1, Susan L. Epstein1,2 1 Department of Computer Science, Hunter College of The City University of New York 2 Department of Computer Science, The Graduate Center of The City University of New York [email protected], [email protected], [email protected] error rate. The minimum number of insertions, deletions and Abstract substitutions required for transformation of one sequence into As graphemetophoneme methods proliferate, their careful another is the Levenshtein distance [3]. Phoneme error rate evaluation becomes increasingly important. This paper ex (PER) is the Levenshtein distance between a predicted pro plores a variety of metrics to compare the automatic pronunci nunciation and the reference pronunciation, divided by the ation methods of three freelyavailable graphemetophoneme number of phonemes in the reference pronunciation. Word er packages on a large dictionary. Two metrics, presented here ror rate (WER) is the proportion of predicted pronunciations for the first time, rely upon a novel weighted phonemic substi with at least one phoneme error to the total number of pronun tution matrix constructed from substitution frequencies in a ciations. Neither WER nor PER, however, is a sufficiently collection of trusted alternate pronunciations. These new met sensitive measurement of the distance between pronunciations. rics are sensitive to the degree of mutability among phonemes. Consider, for example, two pronunciation pairs that use the An alignment tool uses this matrix to compare phoneme sub ARPAbet phoneme set [4]: stitutions between pairs of pronunciations. S OW D AH S OW D AH Index Terms: graphemetophoneme, edit distance, substitu S OW D AA T AY B L tion matrix, phonetic distance measures On the left are two reasonable pronunciations for the English word “soda,” while the pair on the right compares a pronuncia 1. -
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars Diaa Mohamed Fayed1 Aly Aly Fahmy2 Mohsen Abdelrazek Rashwan3 Wafaa Kamel Fayed4 1 Department of Computer Science, Faculty of Computers and Information, 2 Department of Computer Science, Faculty of Computers and Information, 3 Department of EECE, Faculty of Engineering, 4 Department of Arabic Language and Literatures, Faculty of Arts, Cairo University, [email protected], [email protected], [email protected], [email protected] ABSTRACT: Dictionaries are rich sources of lexical information about words that is required for many applications of human language technology. However, publishers prepare printed dictionaries for human usage not for machine processing. This paper presents a method to structure partly a machine-readable version of the Arabic-English Al-Mawrid dictionary. The method converted the entries of Al-Mawrid from a stream of words and punctuation marks into hierarchical structures. The hierarchical structure or tree structure expresses the components of each dictionary entry in explicit format. The components of an entry are subentries and each subentry consists of defining phrases, domain labels, cross-references, and translation equivalences. We designed the proposed method as cascaded steps where parsing is the main step. We implemented the parser through the parsing expression grammar formalism. In conclusion, although Arabic dictionaries do not have microstructure standardization, this study demonstrated that it is possible to structure them automatically or semi- automatically with plausible accuracy after inducing their microstructure. Keywords: Al-Mawrid, Arabic-English, Machine-Readable Dictionary, Parsing Expression Grammars Received: 16 November 2013, Revised 19 December 2013, Accepted 24 December 2013 © 2014 DLINE.