Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 7, Issue 2, 2001

Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Results from this manual investigation informs a discussion on the feasibility of automatic extraction of the different forms of expression of the causal relation.

Our work deals with the domain of hispathology. In diagnosis histopathological images are described differently by different observers and even by the same observer at different times. This divergence in the identification of specific morphological features is partly due to varying levels of expertise among pathologists and to differences in subjective analysis and comprehension of pathological images. As linguists and developers of Natural Language Processing (NLP) systems, we started a collaboration with the Medical Informatics Department at the Broussais Hospital in order to explore a new method for corpus-based medical glossary acquisition. We focused our analysis on adjectives because they are the main linguistic category involved in the evaluation process. The first results of this study show the relevance of a corpus-based approach to cope with the “subjective” interpretations given by pathologists when they analyse microscopic images.

Advances in corpus linguistics are of vital importance in terminology. The information obtained from corpora can be used to complement data already codified in dictionaries and termbases. In this article, we describe a framework of linguistic analysis that facilitates the extraction of conceptual information from corpora, and thus contributes to the study and analysis of terminological contexts. We are presently using this methodology in a research project called Oncoterm. One of the objectives of this project is to elaborate a bilingual terminological database, whose conceptual structure is an extension of an existing knowledge resource, the Mikrokosmos Ontology. In our termbase, medical concepts are organized in categories represented by templates, which are systematically applied to all category members. The application of the template to more specific concepts generates values that show the inheritance of knowledge structures within a specialized domain. The definitional information within each term entry is thus totally coherent with the information regarding other terms within the same conceptual category. This is conducive to the specification of a language of terminographic definition, which is concise, consistent and applicable not only to the domain of oncology, but also extensive to other medical domains and other languages.

This paper focuses on the improvement of statistically-extracted phrase lists by applying word alignment approaches to bitext. Such phrase lists serve several tasks such as the compilation of terminology or translation databases. Our investigations are based on the assumption that word alignment favors well-formed phrase structures rather than irregular text segments. If this is the case, word alignment will filter out irregular structures from automatically generated phrase lists. As a result, an improved phrase list, in terms of precision, may be compiled. Furthermore, word alignment approaches can be used to identify additional multi-word units, e.g. multi-word cognates. Our investigations are focused on a Swedish/English text corpus that has been aligned with the Uppsala Word Aligner (UWA). Finally, we describe and apply three approaches to evaluate the automatically generated phrase lists: an evaluation by comparing results with existing reference data (prior reference), an evaluation against given syntactic patterns (prior reference patterns), and a manual evaluation of sample data (posterior reference). The evaluations of the extraction of phrasal terms in English substantiate the assumption: precision has improved significantly with little loss in recall.

This work reflects on what must be the basic object of terminological research in accordance with the needs of users. Firstly, we argue that this object cannot be limited to nominal referential units, that is to say, to terminological units. It has to include all units with specialised meaning in specialised texts. Secondly, we make a distinction between relevant and non-relevant units of specialised meaning for a specific professional purpose. This difference allows us to formulate user profiles for concrete terminological applications. We base our statements on the theoretical foundations of “The Communicative Theory of Terminology” (Cabré 1999, 2000), and test it with two experiments which were carried out in the field of term detection.

This article describes our work to identify and classify terms in the domain of molecular biology according to examples that have been marked up by a domain expert in a corpus of abstracts taken from a controlled search of the Medline database. Automatic acquisition of biomedical term lists has so far been slow due to high variability in both the terms and their classification scheme, which we attribute to the diversity of research disciplines involved. Nevertheless, the explosive growth in online molecular biology literature makes a persuasive case for automating many tasks. This includes acquisition of records for gene-product databases such as SwissProt which are currently updated by human experts, a task that is both time consuming and often highly idiosyncratic. In this article we report results from a tool based on a hidden-Markov model for extracting and classifying terms that can be used as a key component in an information extraction system. We discuss the results in light of lexical, syntactic and semantic properties of terms that were revealed by our study.

The proliferation of specialist texts over recent decades has exacerbated the need for term extraction software to assist terminologists in compiling terminology collections. To this end, an automated approach to English term extraction is presented, which, in keeping with the multidisciplinary working environments of many contemporary terminologists, is designed to be domain independent. Based on observations made of the linguistic features of terms and their linguistic environment in text, this approach identifies single- and multi-word terms spanning a range of word classes. An implementation of the approach (denoted ‘Textprobe’) is described and evaluated by measuring its term extraction efficiency against the manual scanning output of both domain experts and terminologists. Results obtained in the evaluation suggest that a high proportion of single-and multi-word terms can successfully be extracted from special language texts. It is anticipated that the approach will be portable to other European languages.