From key words to key semantic domains

Abstract

This paper reports the extension of the key words method for the comparison of corpora. Using automatic tagging software that assigns part-of-speech and semantic field (domain) tags, a method is described which permits the extraction of key domains by applying the keyness calculation to tag frequency lists. The combination of the key words and key domains methods is shown to allow macroscopic analysis (the study of the characteristics of whole texts or varieties of language) to inform the microscopic level (focussing on the use of a particular linguistic feature) and thereby suggesting those linguistic features which should be investigated further. The resulting 'data-driven' approach presented here combines elements of both the 'corpus-based' and 'corpus-driven' paradigms in corpus linguistics. A web-based tool, Wmatrix, implementing the proposed method is applied in a case study: the comparison of UK 2001 general election manifestos of the Labour and Liberal Democratic parties.

Hard or Soft Science? Conceptualizing Educational Technology through a Lexical Bundle Analytical Approach

“…For instance, thank you very much is a recurrent spoken-form bundle in daily conversation to express one's utmost gratitude to the addressed person, while as can be seen is a common written-form bundle in academic prose to make readers aware of the research results shown in tables or figures. There are a number of parallel terms for denoting similar notions of lexical bundles in pertinent literature, such as clusters (Hyland, 2008a, b), n-grams (Stubbs, 2007), lexical phrases (Li & Schmitt, 2009), prefabricated patterns (Granger, 1998), formulaic sequences (Simpson-Vlach & Ellis, 2010), sentence stems (Pawley & Syder, 1983), extended collocations (Cortes, 2004), and multi-word expressions (Rayson, 2008).…”

Discovering affect-laden requirements to achieve system acceptance

“…Corpus linguistics combined with NLP can be used to infer properties of a document by comparison to a large corpus of text whose properties are known a priori; this has also found application in RE [27], [30], particularly for abstraction identification [12]. Here, shallow semantics can be inferred from lexical form and context to (e.g.)…”

“…In both modes, we performed a manual analysis and a supervised semi-automatic analysis using Wmatrix [27].…”

Using Wmatrix to Explore Discourse of Economic Growth

“…Currently, the lexicon contains nearly 37,000 words and the idiom list contains over 16,000 multi-word units. An idiom list enables the corpus tool to identify any idiomatic expressions, usually non-decompositional sequences, and to assign a special set of tags to the words in that particular idiomatic phrase to denote a part-of-speech relation above the level of the word (Rayson, 2008). The semantic tagset, loosely based on the LongmanLexicon of Contemporary English, has a multitier structure with 21 major semantic fields and more than 232 subdivisions.…”

“…One way to address the above problems is to conduct key part-of-speech and key semantic analyses which give rise to analytical categories that (1) are fewer than keywords, thus reducing the number of categories a researcher needs to take into account, and (2) group lower frequency words which might not appear as keywords individually and could thus be overlooked (Rayson, 2008).…”