Doing Corpus Linguistics

Crawford, William J.; Csomay, Enikó

doi:10.4324/9781315775647

Cited by 39 publications

(33 citation statements)

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“…By way of illustration, some particular adjectives or verbs are likely to co-occur with one synonym rather than another, which lends support to a number of studies (e.g. Aroonmanakun, 2015;Chung, 2011;Crawford and Csomay, 2016;Jirananthiporn, 2018;Petcharat and Phoocharoensil, 2017).…”

Section: Adjective Collocatessupporting

confidence: 58%

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A Genre and Collocational Analysis of Consequence, Result, and Outcome

Phoocharoensil

2020

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This corpus-based study examines genres and collocation patterns in which the three synonyms 'consequence', 'result', and 'outcome' usually occur. The data on which the study is based is derived from the Corpus of Contemporary American English (COCA). Of all the eight genres currently available in COCA, the three synonyms appear with the highest frequency in academic texts, whereas frequencies are lowest in informal genres, i.e. TV and movie subtitles and fiction. Of pedagogical concern is the fact that the common verb and adjective collocates repeatedly co-occur with the synonymous nouns. Determined by the COCA frequency and the MI value (≥ 3), 'consequence' is often used with verbs and adjectives conveying negative senses, and the typical collocates of 'result' has a clear association with research-oriented contexts. The collocates of 'outcome' have the broadest variety of semantic properties but are not directly related to any specific contexts. It is highly recommended that EFL teachers apply this genre and collocational information to synonym development lessons.

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Section: Adjective Collocatessupporting

confidence: 58%

“…collocations, formality, distribution across text types (e.g. Crawford and Csomay, 2016;Jirananthiporn, 2018;Petcharat and Phoocharoensil, 2017) without referring to any statistics to confirm statistically significant level of collocational strength, with the exception of Aroonmanakun's (2015) study.…”

Section: Previous Studies On Synonymsmentioning

confidence: 99%

A Genre and Collocational Analysis of Consequence, Result, and Outcome

Phoocharoensil

2020

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“…Corpus linguistic analyses can be used to examine a wide variety of issues including: highlighting the evolution of a discourse type through time; discovering the linguistic differences and similarities among different parts of a document; comparing the structure and content of discourses provided in different languages; identifying the genre characteristics of a text type; and detecting specific characteristics associated with a text type including sentiment, thematic patterns, and important (key) words and themes. The methods are often used to triangulate extant studies that rely on researcher intuition (Baker, ; Crawford & Csomay, ). They may be used as an initial step in an NLP investigation to help understand the corpus and develop resources such as wordlists and topic categories for subsequent use; or the techniques can form the basis of a discourse analysis in their own right.…”

Section: New Horizons In Textual Analysismentioning

confidence: 99%

In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse

El-Haj

Rayson

Walker

et al. 2019

Business Fin & Account

105

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We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of studies applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four tools are named entity recognition (NER), summarization, semantics and corpus linguistics. K E Y W O R D S 10-K, annual reports, computational linguistics, conference calls, corpus linguistics, earnings announcements, machine learning, NLP, semantics 1Information is the lifeblood of financial markets and the amount of data available to decision-makers is increasing exponentially. Bank of England (2015) estimates that 90% of global information has been created during the last decade, (MD&A), whereas practitioners, standard setters and regulators are often interested in more granular issues such as the format and content of specific disclosures, placement of content within the overall reporting package, limits on the use of jargon concerning particular topics, etc. Second, it is not immediately obvious how commonly employed empirical proxies for discourse quality such as readability (Fog index), tone (word-frequency counts) and text re-use (cosine similarity) map into the practical properties of effective communication identified by financial market regulators.With these caveats in mind, we proceed to review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. The median AF study examines 10-K filings using basic content analysis methods such as readability algorithms and keyword counts. The degree of clustering is consistent with the initial phase of the research lifecycle, with agendas shaped as much by ease of data access and implementation as by research priorities. Nevertheless, closer inspection reveals how relatively basic word-level methods have been used to provide richer insights into the properties and effects of financial discourse.Refinements to standard readability metrics, development of domain-specific wordlists, and the use of weighting schemes and text filtering to improve word-sense disambiguation represent welcome advances on naïve unigram word counts. We also acknowledge a move towards the use of more NLP technology in the form of machine learning and topic...

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“…'Collocation' 34,49 is the phenomenon whereby words or 'collocates' 48 habitually co-occur with one another. 50 Collocates can be reckoned through statistical calculations such as LL or mutual information. 51,52 'Concordance lines' 31 display words of interest, 'keywords' or 'nodes' in their context within the text, that is, with a chunk of co-text either side 43 ; the standard display for concordances is termed keyword in context (KWIC) lines.…”

Section: Discussionmentioning

confidence: 99%

“…51,52 'Concordance lines' 31 display words of interest, 'keywords' or 'nodes' in their context within the text, that is, with a chunk of co-text either side 43 ; the standard display for concordances is termed keyword in context (KWIC) lines. 50,53 Concordance lines permit rapid identification of language patterns. 54 All concordance line outputs were read and random samples were used to illustrate patterns; identification codes of transcripts for sample lines were checked to ensure the samples were not drawn from the same one or few participants.…”

Section: Discussionmentioning

confidence: 99%

GP perspectives on hospital discharge letters: an interview and focus group study

et al. 2020

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Background Written discharge communication following inpatient or outpatient clinic discharge is essential for communicating information to the GP, but GPs’ opinions on discharge communication are seldom sought. Patients are sometimes copied into this communication, but the reasons for this variation, and the resultant effects, remain unclear.AimTo explore GP perspectives on how discharge letters can be improved in order to enhance patient outcomes.Design & settingThe study used narrative interviews with 26 GPs from 13 GP practices within the West Midlands, England.MethodInterviews were transcribed and data were analysed using corpus linguistics (CL) techniques.ResultsElements pivotal to a successful letter were: diagnosis, appropriate follow-up plan, medication changes and reasons, clinical summary, investigations and/or procedures and outcomes, and what information has been given to the patient. GPs supported patients receiving discharge letters and expounded a number of benefits of this practice; for example, increased patient autonomy. Nevertheless, GPs felt that if patients are to receive direct discharge letter copies, modifications such as use of lay language and avoidance of acronyms may be required to increase patient understanding.ConclusionGPs reported that discharge letters frequently lacked content items they assessed to be important; GPs highlighted that this can have subsequent ramifications on resources and patient experiences. Templates should be devised that put discharge letter elements assessed to be important by GPs to the forefront. Future research needs to consider other perspectives on letter content, particularly those of patients.

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Doing Corpus Linguistics

Cited by 39 publications

References 0 publications

A Genre and Collocational Analysis of Consequence, Result, and Outcome

A Genre and Collocational Analysis of Consequence, Result, and Outcome

In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse

GP perspectives on hospital discharge letters: an interview and focus group study

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