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The WebGenre Blog: The power of genre applied to digital information. By Marina Santini » Entries tagged with "terminology"

Book Chapter: Genre and Terminology by Margaret Rogers (2000)

Useful insights about the relation between domain-specific lexicons and the corpus-driven approach to terminology Genre and Terminology by Margaret Rogers Chapter in: Analysing Professional Genres Edited by Anna Trosborg, John Benjamins [Pragmatics & Beyond New Series 74] 2000 Googlebook: … Read entire article »

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Request: Corpus-Based Sublanguage Glossary

How to build a glossary of: specialized term = common word automatically? Dear all, I wonder if you have any experience or if you can provide references on how to build automatically  a glossary from genre-specific corpora. The glossary should be made of pairs in the form of: sublangage term = common/familiar word. For instance: anemi = blood deficiency analgesic = painkiller etc. Thanks in advance for suggestions and pointers. Marina   … Read entire article »

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Thesis Review: Cross-Language Ontology Learning

Hjelm, Hans (2009) Cross-language Ontology Learning. Incorporating and Exploiting Cross-language Data in the Ontology Learning Process. Academic dissertation for the Degree of Doctor of Philosophy in Computational Linguistics at Stockholm University, 2009. Permalink: Review by Marina Santini   The PhD thesis ”Cross-language Ontology Learning” presents a framework for automating cross-language ontology creation systems and suggests a setting in which cross-language data can be profitably integrated. The high-level task is to computerize the acquisition of semantic knowledge. In Information Science, ontology is “a way of representing knowledge or structuring the terminology within a domain” (p. 14 ). The thesis focuses on the learning of domain ontologies and limits its scope to studying is-a hierarchies. Ontology learning is the automated acquisition of a domain terminology from raw natural language texts. That is, “given a collection of … Read entire article »

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The Path Forward: From Big Unstructured Data to Contextualized Information

How can we convert massive quantities of unstructured data to structured information? What kind of “structure” do we need for a reliable interpretation of this undomesticated data? I suggest thinking of a text-analytic framework based on “context”. Search keywords, events, entities, sentiments, attitudes, polarities, opinions etc. have a different weight and require a different assessment depending on the kind of texts, the situational context, the  field of discussion, and the authority of the source, as well as on the purpose of use. For example, for an official use, factual texts might have more credibility than opinionated texts. In this respect, press conferences, declarations or announcements by a White House spokesman might be more reliable than newspapers’ speculations or op-ed articles. On the contrary, if we want to test the pulse and … Read entire article »

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