DOCSLIB.ORG

First Steps Towards Building a Medical Lexicon for Spanish with Linguistic

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
First Steps Towards Building a Medical Lexicon for Spanish with Linguistic First Steps towards a Medical Lexicon for Spanish with Linguistic and Semantic Information Leonardo Campillos-Llanos Computational Linguistics Laboratory, Universidad Autonoma´ de Madrid [email protected] Abstract methods (e.g. rule-based and dictionary-based). In order to overcome the data bottleneck, richly- We report the work-in-progress of collecting structured terminological thesauri enhance the an- MedLexSp, an unified medical lexicon for the Spanish language, featuring terms and in- notation and concept normalization of domain cor- flected word forms mapped to Unified Medical pora to be used subsequently in supervised mod- Language System (UMLS) Concept Unique els. More importantly, to achieve comparable Identifiers (CUIs), semantic types and groups. benchmarks, domain resources should integrate First, we leveraged a list of term lemmas and standard terminologies and coding schemes. forms from a previous project, and mapped In this context, we aim at providing a computa- them to UMLS terms and CUIs. To en- rich the lexicon, we used both domain-corpora tional lexicon to be used in the pre-processing of (e.g. Summaries of Product Characteristics text data used in more complex Natural Language and MedlinePlus) and natural language pro- Processing (NLP) tasks. The work here presented cessing techniques such as string distance reports the first steps towards building the Medi- methods or generation of syntactic variants of cal Lexicon for Spanish (MedLexSp). MedLexSp multi-word terms. We also added term vari- is conceived as an unified resource with linguis- ants by mapping their CUIs to missing items tic information (lemmas, inflected forms and part- available in the Spanish versions of standard of-speech), concepts mapped to Unified Medical thesauri (e.g. Medical Subject Headings and R World Health Organization Adverse Drug Re- Language System (hereafter, UMLS) (Boden- actions terminology). We enhanced the vo- reider, 2004) Concept Unique Identifiers (CUIs), cabulary coverage by gathering missing terms and semantic information (UMLS types and from resources such as the Anatomical Thera- groups). Figure1 is a sample of the lexicon. peutical Classification, the National Cancer In- MedLexSp is firstly aimed at named entity recog- stitute (NCI) Dictionary of Cancer Terms, Or- nition (NER), and it can be used in the pre- phaData, or the Nomenclator´ de Prescripcion´ annotation step of an NER pipeline. It can also for drug names. Part-of-Speech information is being included in the lexicon, and the cur- help lemmatization and feed general-purpose Part- rent version amounts up to 76 454 lemmas and of-Speech taggers applied to medical texts—as 1 203 043 inflected forms (including conjugated done in previous works (Oronoz et al., 2013). Be- verbs, number and gender variants), corre- cause it gathers semantic data of terms, it can ease sponding to 30 647 UMLS CUIs. MedLexSp relation extraction tasks. is distributed freely for research purposes. Our work makes several contributions. We 1 Introduction provide a resource to be distributed for research purposes in the BioNLP community. MedLexSp Current machine-learning and deep-learning- includes inflected forms (singular/plural, mascu- based methods are data-intensive; however, in do- line/feminine) and conjugated verb forms of term mains such as Medicine, sufficient data are not al- lemmas, which are mapped to UMLS Concept ways available—due to ethical concerns or privacy Unique Identifiers. Verb terms are also mapped issues, especially when dealing with Patient Pro- to Concept Unique Identifiers; this is the line of tected Information. Moreover, some tasks demand current works for expanding terminologies by in- high precision outcomes, which either need super- vised approaches with annotated data or hybrid 1https://zenodo.org/record/2621286 152 Proceedings of the BioNLP 2019 workshop, pages 152–164 Florence, Italy, August 1, 2019. c 2019 Association for Computational Linguistics Figure 1: Sample of the MedLexSp lexicon. In each entry, field 1 is the UMLS CUI of the entity; field 2, the lemma; field 3, the variant forms; field 4, the Part-of-Speech; field 5, the semantic types(s); and field 6, the semantic group. cluding verb terms (Thompson et al., 2011; Chiu (MeSH) are developed by the National Library of et al., 2019). We also added inflected terms Medicine for indexing biomedical articles. Lastly, from MedlinePlus terms, OrphaData (INSERM, the World Organization of Family Doctors pro- 2019), the National Cancer Institute (NCI) Dic- duced the International Classification of Primary tionary of Cancer Terms, or the Nomenclator de Care (ICPC) to classify data aimed at family and prescripcion´ (AEMPS, 2019), a knowledge base primary care physicians (WONCA, 1998). of medical drugs prescribed in Spain. Medical taxonomies or classifications gather es- Section2 gives an overview of medical thesauri, sential domain knowledge.Some examples are the and Section3 describes the methods used to gather International Classification of Diseases vs. 10 terms (both corpora and NLP techniques), map (ICD-10) (WHO, 2004), or the Anatomical Thera- them to UMLS CUIs, and enrich the lexicon. Sec- peutical Chemical (ATC) classification of pharma- tion4 reports descriptive statistics of the current cological substances (WHO, 2019). version, and Section5, the results of an evaluation conducted during development. We discuss some 2.2 Medical Lexicons limitations and conclude in Section6. Medical lexicons provide a structured represen- 2 Background and Context tation of terms and their linguistic information (lemmas, inflection, or surface variants); hence, 2.1 Health thesauri and taxonomies they are essential for NLP tasks. Unlike medi- Medical thesauri and controlled vocabularies ag- cal thesauri or classifications, they do not register gregate listings of domain terms, and also gather term hierarchies, classifications nor ontological re- information about the type of term (e.g. syn- lations, but they can encode semantic information onym or preferred term), a semantic descriptor and, occasionally, argument structure and corpus- (e.g. DRUG or FINDING), an unique concept iden- based frequency data (Thompson et al., 2011). tifier, and very often a term definition or hier- Initiatives to collect medical lexicons have been archical relations between concepts (e.g. IS A). conducted for English (McCray et al., 1994; John- Thesauri are essential for indexing and populating son, 1999; Davis et al., 2012), German (Weske- databases, domain-specific information retrieval, Heck et al., 2002), French (Zweigenbaum et al., and standardized codification (Cimino, 1996). 2005) or Swedish, even in multilingual initia- Medical thesauri vary according to the applica- tives (Marko´ et al., 2006). For Spanish, some ef- tion (we only give examples related to our work). forts were sparked when a team at the National The Systematized Nomenclature of Medicine Library of Medicine (Divita et al., 2007) started Clinical Terms (SNOMED-CT) (Donnelly, 2006) to build an equivalent of the MetaMap tool (Aron- aims at encoding verbatim mentions in clinical son, 2001). Other teams conducted experiments to texts, and gathers ontological relations between automate the creation of a Spanish MetaMap by concepts. To report drug reactions in pharma- applying machine translation and domain ontolo- covigilance, the World Health Organization cre- gies (Carrero et al., 2008). These initiatives, to the ated the Adverse Reactions Terminology (WHO best of our knowledge, did not achieve a Spanish ART), although the Medical Dictionary for Regu- lexicon available for medical NLP. latory Activities (MedDRA) (Brown et al., 1999) Besides medical lexicons, domain-specific vo- is now preferred. The Medical Subject Headings cabularies were collected for Biology (Thompson 153 et al., 2011). With a different perspective and goal, Corpus-derived medical terminology construc- Consumer Health Vocabularies have been col- tion requires collecting domain texts and applying lected to bridge the gap between patients’ expres- term extraction methods, among others: comput- sions and healthcare professionals’ jargon (Zeng ing graphs of relations between parse trees and and Tse, 2006; Keselman et al., 2007). word dependency similarities (Nazarenko et al., 2001), using parallel corpora to map cognates or 2.3 The Unified Medical Language System aligned words (Sbrissia et al., 2004; Deleger´ et al., The Unified Medical Language System R 2009), linking terms or abbreviations to their def- (UMLS) (Bodenreider, 2004) MetaThesaurus initions or expanded word forms in the text where includes thesauri. The version we used (2018AB) they occur (Yu and Agichtein, 2003; McCrae and gathers 210 sources and over 3.82 millions of Collier, 2008), using dictionary features to iden- concepts in 23 languages. Synonym terms are tify polysemy (Pezik et al., 2008), combining encoded with Concept Unique Identifiers (CUIs); text mining techniques with databases (Thomp- and concepts are assigned a semantic type and son et al., 2011), or having experts review terms, group (McCray et al., 2001). a method which has been used to build disease- specific vocabularies (Wang et al., 2016). 2.4 Methods for Creating Medical Lexicons Approaches based on the Firthian Distribu- We will restrict us here to a shallow overview of tional hypothesis exploit distributional similarity approaches and will not consider taxonomy nor metrics (Carroll et al., 2012). Among them, more ontology building. Methods for widening medi- recent distributional semantics
Load more

Recommended publications
  • A Method for Automatically Building and Evaluating Dictionary Resources
    A Method for Automatically Building and Evaluating Dictionary Resources Smaranda Muresan∗, Judith Klavansy ∗Computer Science Department, Columbia University 500 West 120th, New York, USA [email protected] yCenter for Research on Information Access, Columbia University 535 West 114th St, New York, USA [email protected] Abstract This paper describes a method toward automatically building dictionaries from text. We present DEFINDER, a rule-based system for extraction of definitions from on-line consumer-oriented medical articles. We provide an extensive evaluation on three dimensions: i) performance of the definition extraction technique in terms of precision and recall, ii) quality of the built dictionary as judged both by specialists and lay users, iii) coverage of existing on-line dictionaries. The corpus we used for the study is publicly available. A major contribution of the paper is the range of quantitative and qualitative evaluation methods. 1. Introduction used in the context of summarization of technical articles to Most machine readable dictionaries or glossaries are ei- provide explanation of the technical terms in lay language. ther manually built by human experts or transformed in Our system, was extensively evaluated. First we eval- electronic forms from hard-copy versions through an ex- uated the performance of the definition extraction method pensive digitization process. Also for some particular do- by comparing the results against a gold standard in terms mains, such as medical domain, the effort is concentrated in of precision and recall. Second, we evaluated the quality of building technical dictionaries for specialists that are of lit- the dictionary as judged both by specialists and lay users.
    [Show full text]
  • Biomedical Entity Representations with Synonym Marginalization
    Biomedical Entity Representations with Synonym Marginalization Mujeen Sung Hwisang Jeon Jinhyuk Leey Jaewoo Kangy Korea University fmujeensung,j hs,jinhyuk lee,[email protected] Abstract Unlike named entities from general domain text, typical biomedical entities have several different Biomedical named entities often play impor- surface forms, making the normalization of biomed- tant roles in many biomedical text mining ical entities very challenging. For instance, while tools. However, due to the incompleteness two chemical entities ‘motrin’ and ‘ibuprofen’ be- of provided synonyms and numerous varia- tions in their surface forms, normalization of long to the same concept ID (MeSH:D007052), biomedical entities is very challenging. In this they have completely different surface forms. paper, we focus on learning representations of On the other hand, mentions having similar sur- biomedical entities solely based on the syn- face forms could also have different meanings onyms of entities. To learn from the incom- (e.g. ‘dystrophinopathy’ (MeSH:D009136) and plete synonyms, we use a model-based candi- ‘bestrophinopathy’ (MeSH:C567518)). These ex- date selection and maximize the marginal like- amples show a strong need for building latent rep- lihood of the synonyms present in top candi- dates. Our model-based candidates are itera- resentations of biomedical entities that capture se- tively updated to contain more difficult neg- mantic information of the mentions. ative samples as our model evolves. In this In this paper, we propose a novel framework for way, we avoid the explicit pre-selection of learning biomedical entity representations based on negative samples from more than 400K can- the synonyms of entities.
    [Show full text]
  • Study Guide Medical Terminology by Thea Liza Batan About the Author
    Study Guide Medical Terminology By Thea Liza Batan About the Author Thea Liza Batan earned a Master of Science in Nursing Administration in 2007 from Xavier University in Cincinnati, Ohio. She has worked as a staff nurse, nurse instructor, and level department head. She currently works as a simulation coordinator and a free- lance writer specializing in nursing and healthcare. All terms mentioned in this text that are known to be trademarks or service marks have been appropriately capitalized. Use of a term in this text shouldn’t be regarded as affecting the validity of any trademark or service mark. Copyright © 2017 by Penn Foster, Inc. All rights reserved. No part of the material protected by this copyright may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the copyright owner. Requests for permission to make copies of any part of the work should be mailed to Copyright Permissions, Penn Foster, 925 Oak Street, Scranton, Pennsylvania 18515. Printed in the United States of America CONTENTS INSTRUCTIONS 1 READING ASSIGNMENTS 3 LESSON 1: THE FUNDAMENTALS OF MEDICAL TERMINOLOGY 5 LESSON 2: DIAGNOSIS, INTERVENTION, AND HUMAN BODY TERMS 28 LESSON 3: MUSCULOSKELETAL, CIRCULATORY, AND RESPIRATORY SYSTEM TERMS 44 LESSON 4: DIGESTIVE, URINARY, AND REPRODUCTIVE SYSTEM TERMS 69 LESSON 5: INTEGUMENTARY, NERVOUS, AND ENDOCRINE S YSTEM TERMS 96 SELF-CHECK ANSWERS 134 © PENN FOSTER, INC. 2017 MEDICAL TERMINOLOGY PAGE III Contents INSTRUCTIONS INTRODUCTION Welcome to your course on medical terminology. You’re taking this course because you’re most likely interested in pursuing a health and science career, which entails ­proficiency­in­communicating­with­healthcare­professionals­such­as­physicians,­nurses,­ or dentists.
    [Show full text]
  • 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.
    [Show full text]
  • Lesson 1 Revised
    Name _____________________________________________Date ______________ Period ___ Medical Terminology Exercises in Etymology Throughout its history, the English language has become enriched by borrowings from other languages, particularly Latin, Greek, and French. As the French language itself is a modified form of Latin, many of these borrowed words ultimately come from Greek, since many words from this language had passed into Latin. One such French word is migraine . The word is derived from Latin hemicrania , which borrowed from the Greek hemikrania , pain on one side of the head, from the prefix hemi-, half, and kranion, skull. The ancient Greeks used to smear poison on their arrowheads for use in hunting, and this poison was called toxicon pharmakon ( toxon, bow, archery ; pharmakon , drug); thus, the meaning of the modern word toxic. A toxicologist is one skilled in the study of poisons, while a toxophilite is a lover ( philos ) of archery. The suffix –osis indicates an abnormal condition : neurosis, psychosis ( psych , mind). When affixed to a combining form indicating an organ or part of the body, it usually indicates a non-inflammatory diseased condition: nephrosis, gastrosis ( gaster , stomach). Following the combining form CYT-, cell, it means an abnormal increase in number of the type of cell indicated: leukocytosis, erythrocytosis . Following the combining form for an adjective, it indicates the abnormality characterized by the meaning of the adjective: stenosis: narrowing of a passage; sclerosis : hardening of tissues; cyanosis : bluish discoloration of a part. There are a few words ending in –osis that have specialized meanings: anastomosis : a surgical or pathological connection between two passages; exostosis : a bony growth arising from the surface of a bone; aponeurosis : a sheet of tissue connecting muscles to bones; symbiosis : the living together in close association of two organisms of different species; antibiosis : the association between two organisms when one is harmful to the other.
    [Show full text]
  • WME 3.0: an Enhanced and Validated Lexicon of Medical Concepts
    WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts Anupam Mondal1 Dipankar Das1 Erik Cambria2 Sivaji Bandyopadhyay1 1Department of Computer Science and Engineering 2School of Computer Science and Engineering Jadavpur University, Kolkata, India Nanyang Technological University, Singapore [email protected], [email protected] [email protected], 1sivaji cse [email protected] Abstract However, medical text is in general unstructured since doctors do not like to fill forms and pre- Information extraction in the medical do- fer free-form notes of their observations. Hence, main is laborious and time-consuming due a lexical design is difficult due to lack of any to the insufficient number of domain- prior knowledge of medical terms and contexts. specific lexicons and lack of involve- Therefore, we are motivated to enhance a med- ment of domain experts such as doctors ical lexicon namely WordNet of Medical Events and medical practitioners. Thus, in the (WME 2.0) which helps to identify medical con- present work, we are motivated to de- cepts and their features. In order to enrich this sign a new lexicon, WME 3.0 (WordNet lexicon, we have employed various well-known of Medical Events), which contains over resources like conventional WordNet, SentiWord- 10,000 medical concepts along with their Net (Esuli and Sebastiani, 2006), SenticNet (Cam- part of speech, gloss (descriptive expla- bria et al., 2016), Bing Liu (Liu, 2012), and nations), polarity score, sentiment, sim- Taboada’s Adjective list (Taboada et al., 2011) ilar sentiment words, category, affinity and a preprocessed English medical dictionary1 on score and gravity score features.
    [Show full text]
  • Medical Terminology (3 Units) CSU [Formerly Health Education 41]
    Prepared by: D. Headrick Reviewed by: G. Golling Date prepared: Spring 2014 Text update: September 29, 2014 C&GE approved: May 12, 2014 Board approved: June 11, 2014 Semester Effective: Spring 2015 Health Education (HLED) 1541 Medical Terminology (3 Units) CSU [formerly Health Education 41] Prerequisite: None Total Hours: 48 hours lecture Catalog Description: The focus of this course is learning the origin, correct spelling, pronunciation, meaning and current usage of common medical terms and their application to clinical records and reports. Specific emphasis is on root words, prefixes, suffixes, stems, combining forms, medical abbreviations, symbols, and terms commonly used in patient's records and laboratory reports. Type of Class/Course: Degree Credit Text: Jones, Betty Davis. Comprehensive Medical Terminology, 4th ed. Clifton Park: Cengage, 2011. Print. Rice, Jane. Medical Terminology for Healthcare Professionals with MyMediccalTerminologyLab. 8th ed. New Jersey: Prentice Hall, 2014. Print. Additional Instructional Materials: Medical Terminology Interactive online program Mosby’s Pocket Dictionary of Medicine: Nursing & Health Professions. St. Louis: Elsevier, 2010. Print. Course Objectives: By the end of the course, a successful student will be able to: 1. Identify and use word roots, prefixes, and suffixes to build medical terms, 2. Identify the function of combining vowels in the construction of medical terms, 3. Identify the building blocks, organ systems, and cavities of the body, 4. Identify anatomical planes, regions, and directional terms used to describe areas of the body, 5. Breakdown and define medical terms associated with anatomy and physiology of the body, and 6. Breakdown and define common medical terms used for symptoms, diseases, disorders, procedures, treatments, and devices associated with each body system and specialty areas.
    [Show full text]
  • A Study of Chinese Medical Students As Dictionary Users and Potential Users for an Online Medical Termfinder
    Lexicography ASIALEX https://doi.org/10.1007/s40607-017-0031-9 ORIGINAL PAPER A study of Chinese medical students as dictionary users and potential users for an online medical termfinder Jun Ding1 Received: 2 May 2017 / Accepted: 12 November 2017 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract This paper examines the dictionary use of Chinese medical students as ESP learners and their trial experience with the online Health Termfinder (HTF), a specialized dictionary tool for medical terms. Empirical data were collected first from a survey among a group of medical students at Fudan University on their previous dictionary-use behaviors and initial response to the introduction to the HTF and its bilingualization, and second from an assignment of using the bilingualized HTF for reading comprehension with the same group of students. The survey findings reveal that the medical students in general are aware of the importance of dictionaries in their ESP learning in spite of the alarming fact that the majority of them have not even used any English medical dictionaries (monolingual or bilin- gual). They are thus quite open to the idea of using an online termbank of spe- cialized medical terms. But the HTF assignment findings also show that proper guidance and training is necessary for the termbank to be made actually useful for the lexical needs of Chinese ESP learners. It is also expected that the students’ feedback on their HTF experience, especially their demand for the termbank to include more cross-disciplinary medical terms, could be taken into consideration by the HTF builders.
    [Show full text]
  • Examining the Usefulness of Medical Bilingual Dictionaries for Translation Purposes
    International Journal of Linguistics ISSN 1948-5425 2019, Vol. 11, No. 6 Examining the Usefulness of Medical Bilingual Dictionaries for Translation Purposes Fatima Al Qaisiya (Corresponding author) Department of English Language and Literature The University of Jordan, Jordan E-mail: [email protected] Rajai Rasheed Al-Khanji University of Jordan, Jordan Received: October 29, 2019 Accepted: November 11, 2019 Published: December 22, 2019 doi:10.5296/ijl.v11i6.15723 URL: https://doi.org/10.5296/ijl.v11i6.15723 Abstract This paper aims at examining the effectiveness of four medical bilingual English-Arabic dictionaries for translational purposes. This is done by investigating the provided information used in the presentation of a number of medical words in the examined dictionaries. The results reveal an inconsistency in the presentation of the selected words in the dictionaries; which might be correlated to the lack of provision policies given by the compilers of the dictionaries. Moreover, an inadequacy in the provision of semantic, pragmatic, and encyclopedic information was noticed which would be inadequate for translational purposes. However, it was found that the Unified Medical dictionary covered more types of information like the provision of encyclopedic illustrations and pictorial illustration. Keywords: Medical words, Bilingual medical dictionaries, Dictionary and translation 1. Introduction The disciplines of translation and lexicography are linked to each other in 'give and take' operations (Hartmann, 2004). In this regard, translation is considered as the supplier of translation equivalents to bilingual dictionaries and a customer of information provided by lexicographers to translators, (Hartmann, 2004). In his article 'Lexicography and Translation', www.macrothink.org/ijl 229 International Journal of Linguistics ISSN 1948-5425 2019, Vol.
    [Show full text]
  • Medical Language – a Unique Linguistic Phenomenon
    JAHR Vol. 10/1 No. 19 2019 Professional Article / Stručni članak Received / Zaprimljen: 15. 4. 2019. https://doi.org/10.21860/j.10.1.7 Božena Džuganová* Medical Language – A Unique Linguistic Phenomenon SUMMARY Medical language is the language used by medical experts in their professional communication and incorporates more than 2,500 years of a development influenced mostly by Greek and Latin medical traditions. Its specific features and characteristics are studied from various aspects. It is closely connected with the immense development of technology and science that brings new concepts to the language; medical vocabulary is an open and continually changing phenomenon and its units often acquire new meanings. Learning English medical language that has become a lingua franca during the last few decades creates certain obstacles for learners in the form of collocations, irregular forms, existence of synonyms, doublets, abbreviations, false friends, etc. To manage medical language at an appropriate level requires looking for the most convenient teaching and learning strategies. Good proficiency of English medical language opens new horizons to medical professionals and offers various options of its application in practice. As an international means of communication it slowly penetrates into national medical vocabularies worldwide. Keywords: English medical terminology, English for medical purposes, aspects of medical terminology, pitfalls in learning/teaching process, learning strategies. Introduction “Medicine uses one lingua franca but speaks with many tongues. Just as Latin emerged after the Renaissance beside the regional European languages as the unifying language of the healing arts, so has English now assumed a leading role as the international language of medicine” (Baethge, 2008, p.
    [Show full text]
  • From Wordnet to Medical Wordnet
    Journal of Biomedical Informatics 39 (2006) 321–332 www.elsevier.com/locate/yjbin Towards new information resources for public health—From WORDNET to MEDICALWORDNET Christiane Fellbaum a,b,*, Udo Hahn c, Barry Smith d,e a Department of Psychology, Princeton University Green Hall, Washington Rd., Princeton, NJ 08544, United States b Berlin-Bradenburg Academy of Science, Berlin, Germany c Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-Universita¨t Jena, Jena, Germany d IFOMIS, Universita¨t des Saarlandes, Saarbru¨cken, Germany e Department of Philosophy, State University of New York at Buffalo, USA Received 21 July 2005 Available online 24 October 2005 Abstract In the last two decades, WORDNET has evolved as the most comprehensive computational lexicon of general English. In this article, we discuss its potential for supporting the creation of an entirely new kind of information resource for public health, viz. MEDICALWORDNET. This resource is not to be conceived merely as a lexical extension of the original WORDNET to medical terminology; indeed, there is already a considerable degree of overlap between WORDNET and the vocabulary of medicine. Instead, we propose a new type of repository, consisting of three large collections of (1) medically relevant word forms, structured along the lines of the existing Princeton WORDNET; (2) medically validated propositions, referred to here as medical facts, which will consti- tute what we shall call MEDICALFACTNET; and (3) propositions reflecting laypersonsÕ medical beliefs, which will constitute what we shall call the MEDICALBELIEFNET. We introduce a methodology for setting up the MEDICALWORDNET. We then turn to the discus- sion of research challenges that have to be met to build this new type of information resource.
    [Show full text]
  • English Medical Terminology – Different Ways of Forming Medical Terms
    JAHR Vol. 4 No. 7 2013 Original scientific article Božena Džuganová* English medical terminology – different ways of forming medical terms ABSTRACT In medical terminology, two completely different phenomena can be seen: 1. precisely worked-out and internationally standardised anatomical nomenclature and 2. quickly devel- oping non-standardised terminologies of individual clinical branches. While in the past new medical terms were mostly formed morphologically by means of derivation and composition from Latin and Greek word-forming components, nowadays it is the syntactic method which prevails – the forming of terminological compounds that subsequently turn into abbrevia- tions. Besides the most frequent ways of term formation, there are also some marginal ways, the results of which are acronyms, backcronyms, eponyms, toponyms, mythonyms etc. To understand the meaning of these rather rare medical terms requires us to become familiar with their etymology and motivation. In our paper we will take a look at individual ways of word-formation with focus on marginal procedures. Keywords: English medical terminology, derivation, composition, compound terms, abbre- viations, acronyms, backronyms, eponyms, toponyms, mythonyms In the last century clinical medicine developed into many new branches. Internal medicine for example started to specialise in cardiology, endocrinology, gastroenter- ology, haematology, infectology, nephrology, oncology, pulmonology, rheumatology etc. All this could happen thanks to the great development of science and technolo- gy. New diagnostic devices and methods were invented, e.g. computer tomography, sonograph, mammograph, laparoscope, endoscope, colonoscope, magnetic reso- * Correspondence address: PhDr. Božena Džuganová, PhD., Comenius University, Jessenius Faculty of Medicine, Department of Foreign Languages, Martin, Slovakia, e-mail: [email protected] 55 JAHR Vol.
    [Show full text]