Abstract
EcoLexicon is a terminological knowledge base on environmental science, whose design permits the geographic contextualization of data. For the geographic contextualization of landform concepts, this paper presents a semi-automatic method for extracting terms associated with named rivers (e.g., Mississippi River). Terms were extracted from a specialized corpus, where named rivers were automatically identified. Statistical procedures were applied for selecting both terms and rivers in distributional semantic models to construct the conceptual structures underlying the usage of named rivers. The rivers sharing associated terms were also clustered and represented in the same conceptual network. The results showed that the method successfully described the semantic frames of named rivers with explanatory adequacy, according to the premises of Frame-Based Terminology.
Highlights
EcoLexicon is a multilingual, terminological knowledge base (TKB) on environmental science that is the practical application of Frame-Based Terminology (Faber 2012)
To extract knowledge for the semantic frames or conceptual structures (Faber 2012) that underlie the usage of named rivers in coastal engineering texts, a semi-automated method for the extraction of terms and semantic relations was devised
The semantic relations linking concepts in the semantic frames were manually extracted by querying the corpus in Sketch Engine, and analyzing knowledge-rich contexts
Summary
EcoLexicon (http://ecolexicon.ugr.es) is a multilingual, terminological knowledge base (TKB) on environmental science that is the practical application of Frame-Based Terminology (Faber 2012). Since most concepts designated by environmental terms are multidimensional (Faber 2011), the flexible design of EcoLexicon permits the contextualization of data so that they are more relevant to specific subdomains, communicative situations, and geographic areas (León-Araúz et al 2013). This paper presents a semi-automatic method of extracting terms associated with named rivers (e.g., Nile River) as a type of landform from a corpus of English coastal engineering texts. The following subsections provide the motivation for the research and the background on distributional semantic models.
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