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The dynamics and systematicity of terminology: this book addresses these essential and intriguing aspects of terminology, by using quantitative methodologies which have been underutilized in the field to date. Through the analysis of the Japanese terminologies of six domains and with special reference to the dynamic behaviour and the status of borrowed and native morphemes, the book reveals: (a) how borrowed and native morphemes contribute to the construction of these terminologies, and how these contributions are likely to change as the terminologies grow; (b) how borrowed and native morphemes contribute to the systematicity or systematic representation of conceptual systems; and (c) how borrowed and native morphemes are related to each other and to what extent they are mixed in constructing terminologies. It also examines the epistemological implications of applying these quantitative methodologies, which leads back to such essential questions as the relationship between terminology as a whole and individual terms and what we understand terms to be when we talk about the growth of terminologies. The book should be of interest to a wide audience, including theoretical terminologists, terminographers, quantitative linguists, computational linguists, lexicologists and lexicographers.

Since the advent of the computer, terminology management can be carried out by almost anyone who has learnt to use a computer. Terminology management has proved to be an efficient tool in international communications in industry, education and international organisations. Software packages are readily available and international corporations often have their own terminology database. Following these developments, translators and terminologists are confronted with a specialised form of information management involving compilation and standardisation of vocabulary, storage, retrieval and updating.A Practical Course in Terminology Processing provides the key to methods of terminology management for the English language, for general and specific purposes. This unique course has been developed on the basis of years of teaching experience and research at the University of Manchester Institute of Science and Technology (UMIST, UK) and is particularly suitable for translation courses, freelance translators, technical writers, as well as for non-linguists who are confronted with terminology processing as part of their profession.The 1996 reprint of the paperback edition includes an index.

The computational approach of this book is aimed at simulating the human ability to understand various kinds of phrases with a novel metaphoric component. That is, interpretations of metaphor as literal paraphrases are based on literal meanings of the metaphorically used words. This method distinguishes itself from statistical approaches, which in general do not account for novel usages, and from efforts directed at metaphor constrained to one type of phrase or to a single topic domain. The more interesting and novel metaphors appear to be based on concepts generally represented as nouns, since such concepts can be understood from a variety of perspectives. The core of the process of interpreting nominal concepts is to represent them in such a way that readers or hearers can infer which aspect(s) of the nominal concept is likely to be intended to be applied to its interpretation. These aspects are defined in terms of verbal and adjectival predicates. A section on the representation and processing of part-sentence verbal metaphor will therefore also serve as preparation for the representation of salient aspects of metaphorically used nouns. As the ability to process metaphorically used verbs and nouns facilitates the interpretation of more complex tropes, computational analysis of two other kinds of metaphorically based expressions are outlined: metaphoric compound nouns, such as \"idea factory\" and, together with the representation of inferences, modified metaphoric idioms, such as \"Put the cat back into the bag\".

Terms in Context applies the methodology that has been developed over the last two decades in corpus linguistics to the relatively new and still little developed field of corpus-based terminography. While corpora are already being used by some terminologists for the identification of terms and retrieval of contextual fragments, this book describes the first attempt to use corpora for terminography in much the same way as large general reference corpora are already being used for general language lexicography. The author goes beyond the standard problem of identifying terms as opposed to non-terminological lexical items in text and focuses on identifying metalanguage patterns which point to the presence in text of (parts of) reusable definitions of terms. The author examines these patterns and shows how the information which they contain can be retrieved and used as input for terminological entries.Terms in Context should be of interest to 'traditional' terminologists who have not previously considered adopting a corpus-based approach to their work or at least not on the scale proposed here; to 'modern' terminologists who use text primarily for the identification of terms and the retrieval of contextual examples; to those in the corpus linguistic community who have hitherto used general language corpora for the purposes of lexicography and have not previously considered using special purpose corpora for more specific lexicography studies; and to academics in the ESP/LSP community who are interested in showing students how to use text as a means of ascertaining the meaning of terms.

This paper presents an original natural language processing (NLP) approach for building of back-of-the-book indexes. Our indexing system, IndDoc, exploits some terminological tools and automatically builds an index draft of the analysis of the document text. The indexer then has to validate that index draft through a dedicated interface. This approach has been tested on several documents with promising results. Relying on our experience in developing and testing the IndDoc indexing system, we aim at assessing the contribution of terminological analysis as well as the level of maturity that computational terminology has reached in the indexing perspective.

In this paper we describe term extraction from full length journal articles in the domain of crop husbandry for the purpose of producing abstracts automatically. Initially, candidate terms are extracted which occur in one of a number of fixed lexical environments, as found by a system of contextual templates which assigns a semantic role indicator to each candidate term. Candidate terms which can be lexically validated - that is, whose constituent words and structure conform to a simple grammar for their assigned role - receive an enhanced weight. The grammar for lexical validation was derived from a training corpus of 50 journal articles. Selected terms may be used to generate a short abstract which indicates the subject matter of the paper. We also describe a method for compiling a list of sequences which indicate the statistical findings of an experiment, in particular the interrelationships between terms. Such word sequences, when extracted and appended to an indicative abstract, will produce an informative abstract which describes specific research findings in addition to the subject matter of the paper.

PurposeBreast cancer surgical techniques are evolving. Few studies have analyzed national trends for the multitude of surgical options that include partial mastectomy (PM), mastectomy without reconstruction (M), mastectomy with reconstruction (M+R), and PM with oncoplastic reconstruction (OS). We hypothesize that the use of M is declining and likely correlates with the rise of surgery with reconstructive options (M+R, OS).MethodsA retrospective cohort analysis was conducted using the ACS-NSQIP database from 2005 to 2016 and ICD codes for IBC and DCIS. Patients were then grouped together based on current procedural terminology (CPT) codes for PM, M, M+R, and OS. In each group, categories were sorted again based on additional reconstructive procedures. Data analysis was conducted via Pearson’s chi-squared test for demographics, linear regression, and a non-parametric Mann- Kendall test to assess a temporal trend.ResultsThe patient cohort consisted of 256,398 patients from the NSQIP data base; 197,387 meet inclusion criteria diagnosed with IBC or DCIS. Annual breast surgery trends changed as follows: PM 46.3–46.1% (p = 0.21), M 35.8–26.4% (p = 0.001), M+R 15.9–23.0% (p = 0.03), and OS 1.8–4.42% (p = 0.001). Analyzing the patient cohort who underwent breast conservation, categorical analysis showed a decreased use of PM alone (96–91%) with an increased use of OS (4–9%). For the patient cohort undergoing mastectomy, M alone decreased (69–53%); M+R with muscular flap decreased (9–2%); and M+R with implant placement increased (20–40%)—all three trends p < 0.0001.ConclusionThe modern era of breast surgery is identified by the increasing use of reconstruction for patients undergoing breast conservation (in the form of OS) and mastectomy (in the form of M+R). Our study provides data showing significant trends that will impact the future of both breast cancer surgery and breast training programs.

It is widely acknowledged that natural language processing, as an indispensable means for information technology, requires the strong support of world knowledge as well as linguistic knowledge. This book is a theoretical exploration into the extra-linguistic knowledge needed for natural language processing and a panoramic description of HowNet as a case study.

Background Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. Results This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence , substance abuse , and respiratory depression ). Conclusions This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.