Parsing SBVR-Based Controlled Languages

Author(s):  
Mathias Kleiner ◽  
Patrick Albert ◽  
Jean Bézivin
Keyword(s):  
Author(s):  
Peter Clark ◽  
William R. Murray ◽  
Phil Harrison ◽  
John Thompson
Keyword(s):  

Author(s):  
Richard I. Kittredge

This article deals with the topic of sublanguage, the original language grammar subset, which informs various text outputs. Despite routine deviance from standard languages, quite often sublanguage grammatical patterns draw heavily from standard languages. Machine translation, database extraction from texts, and natural language generation are some ways of sublanguage processing. The definition of controlled language projects the difference between itself and sublangauge. The former is described as a restricted set of natural language, engineered to facilitate communication between expert native speakers and either non-expert natives or expert non-natives. However, the difference lies in the fact that controlled language is not a natural subset, unlike sublangauge. Unlike sublanguage that works like a general language in not restricting its sentences, controlled language sets an upper limit, typically around twenty-five. Contrast between controlled language and sublanguage assumes theoretical importance.


2019 ◽  
Vol 31 (1-2) ◽  
Author(s):  
María Teresa Alarcón Gil ◽  
Sonia Osorio Toro ◽  
Gloria Patricia Baena Caldas

Introduction: the PICO mnemonic is an evidence-based medicine tool that helps formulate the research questions needed to conduct the right search for scientific information. To properly classify this information, controlled languages or thesauruses are used for information retrieval. The aim was to identify whether the PICO search strategy in evidence-based medicine using the MeSH, Emtree and DeCS thesauruses answers a research question in the field of dentistry. Methods: to carry out the PICO strategy, a research question was formulated, identifying the natural language terms for each component of the PICO acronym, which were normalized into the three thesauruses to create the search equations. Results: 43 results were foundon Medline through PubMed, 5 on Embase, and 0 on LILACS. There were 4 original articles that answer the research question, proving to be an effective strategy for finding clinical evidence. Conclusion: this study shows that the strategy helps obtain results to answer the question posed. It should be noted that, in order to successfully search and retrieve information, researchers should use the PICO strategy and get familiar with the thesauruses that help structure search equations in the various bibliographic databases.


As for making databases more intelligent, NTA can be considered an extension of relational algebra to knowledge processing. Besides, we propose an approach to development of search engines, in particular, question-and-answer teaching systems based on controlled languages and algebraic models for representation and processing of question-and-answer texts.


Author(s):  
Richard I. Kittredge

Restricted subsystems of language can arise spontaneously in a subject-matter domain where speech or writing is used for special purposes. Alternatively, language restrictions can be imposed by conscious design. This chapter introduces the phenomenon of natural sublanguage in the first case, and contrasts it with the increasingly important notion of controlled language, which applies in the second case. Many of the successful language processing applications which deal with language meaning are limited to naturally occurring sublanguages. We give examples of natural sublanguages and describe their key properties for automatic processing. One or more related sublanguages may serve as the basis for a controlled language, where standards are introduced to reduce ambiguity, limit complexity, and enforce uniform style.


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