Feature Presentation
Using Word Frequencies to Introduce
Corpora in the Classroom
Jonathan Ferries
Bukkyo University
Classroom use of corpora has long been advocated but remains rare in practice. In thispaper, I summarize the obstacles to corpus use, describe a simple frequency-based methodof analysis that avoids these problems, and offer a gateway to a more sophisticated use ofcorpora to promote more active, independent learning.

In this paper, I describe a two-stage method of using word frequency counts fromonline corpora to provide a simple introduction to using online corpora in theclassroom. Researchers have long advocated using corpora to enable learners toexplore and make their own inductive language discoveries through data-driven
learning (see Johns, 1986). In practice, however, corpora remain rarely used in
the classroom (Boulton, 2017; Breyer, 2009; Ma et al., 2022; O’Keefe & Farr,2003). This is attributed on the teacher side to lack of training in corpus skills(Boulton, 2017; Breyer, 2009; Leńko-Szymańska, 2014), and on the student
side to insufficient knowledge of the metalanguage required to perform corpus
searches (Chang, 2014; Yeh et al., 2007). Studies have also drawn attention to
the problems faced by both teachers and students in analyzing and interpretingconcordance lines (Breyer, 2009; Ma et al., 2022).
Concordance lines are lines of text centered around a nodal word or phrase,and producing them is one of the basic functions of corpus analysis software.
However, they can be challenging to read for the inexperienced (O’Keefe et al.,2007) and are considered more suitable for use with more advanced learners.
This poses a challenge for practitioners in locations such as Japan, where many
learners do not advance beyond the lower intermediate level (Hadley, 2001).
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To address these issues, the method presented below makes use of anotherbasic function of corpus software: calculation of word frequencies. These
calculations are relatively easy to perform, and they produce results that arevisually easy to grasp. I illustrate how this method can be applied with an
example drawn from the domain of English for academic purposes (EAP) that
I have used with Japanese L1 learners of English majoring in sciences at theundergraduate level. With appropriate modifications, this method may be usedwith learners of lower intermediate level and above in other domains of English
for specific purposes (ESP).
However, word frequencies cannot tell us how and in what context a wordor phrase is used. For this, concordance lines showing a certain degree of co-text
are required. The method described below serves only as a relatively accessible
gateway to more sophisticated use of corpora, including concordances, in theclassroom.

Use of “I” in academic writing

My example concerns use of the first-person pronoun in academic writing. This isa topic of clear relevance to EAP students, and one regarding which students may
have received advice in the past. For example, both lower-intermediate textbooks(e.g., Ackert et al., 2014) and writing guides for advanced students (e.g., Bailey,2011) from major publishing houses recommend avoidance of the first personin academic writing. Several corpus-based studies have demonstrated that thisadvice is not always followed in actual academic writing. Hyland (2001) andDobakhti and Hassan (2017), for example, assembled their own large corpora
to reveal extensive use of the first person in research articles and considerable
variation between disciplines.
However, approaches like these that use purpose-built corpora are unfeasible
for ESP classroom use. This is because they use corpora that take time toprepare and specialized software that takes more time to master. With time inthe language curriculum often scarce, students and teachers need ready-madecorpora that are cheap, accessible, and relevant. Several large corpora and corpus
analysis tools are6Using Word Frequencies to Introduce Corpora, OCJSI 5, pages 5-13

now accessible online that meet these requirements. These include theBritish National Corpus (Davies, 2004), the Corpus of Contemporary American
English (COCA) (Davies, 2008), and Lextutor (Cobb, 2017). I chose COCA
because of its size and breadth in content, the simplicity of its search interface,
the availability of instructions on its use in Japanese, and its cost (students canperform up to 50 searches per day for free).
Stage 1: Searching the corpus
The first stage consists of searching the chosen corpus for the features underinvestigation. In my example, the aim was to investigate how academic writers
refer to themselves and their actions in order to determine whether they
avoid using “I” and instead use structures such as the passive voice. This wasoperationalized by searching for occurrences of the pronoun I (as a proxy for firstperson authorial references) and be + past participle (as a proxy for passive-voiceauthorial references) in each of the academic disciplines contained in COCA.
This is potentially the most challenging step for teachers and students unfamiliar
with corpora. Corpora are normally annotated by “tagging” to indicate the partof speech of each word. The purpose of this is to allow users to search for instancesof, for example, will used as a modal verb but not as a noun. Users require someknowledge of these tags in order to perform effective searches. For simplicity, I
used the query syntax i_p* to search for instances of the pronoun I, and thequery syntax are|were [vvn*] to search for passive be + past participle
constructions.1
A search of this kind produces results indicating the frequency of occurrencein each discipline of the features searched for (Figure 1).
These may be converted by students or the teacher to chart form (Figure 2).
This shows, in a visually easy-to-grasp manner, that the advice not to use “I” inacademic writing is not always followed by academics in practice; the first person
appears to be used to some degree in all disciplines, and it is used more frequently
than the passive voice in the Humanities and Philosophy/Religion.2
Owing to the crudity of the proxies and query syntax used, these results
provide only a rough indication of the relative frequencies of use of the first7

Ferries

Figure 1. Selected raw outputs (Source: COCA).

Figure 2. First person and passive voice frequencies by discipline
person and passive voice in different disciplines. Use of pronoun I as a proxyexcluded first-person pronouns in other cases (me, my, us, our), which meansthat the results do not accurately reflect the frequency of use of all forms of firstperson expression. The search for passive be + past participle using the syntax
are|were [vvn*] is likewise limited in scope. It fails to capture, for example,
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Using Word Frequencies to Introduce Corpora, OCJSI 5, pages 5-13

constructions using the more colloquial passive auxiliary get and instances wherean adverb occurs between the auxiliary and past participle (e.g., were frequently
referred to). A second, more in-depth stage of analysis is therefore required.
Stage 2: Manual search
For the second stage, a manual search is made of a smaller sample of texts. In
the case of my example, a manual search was made for all instances of pronoun
I referring to the writer (excluding, for example, instances in quotations andappended questionnaires) and all instances of the passive voice used to describe
actions (including judgments and other mental processes) performed by theauthor.
Given time and other classroom constraints, the manual analysis waslimited to the two disciplines found to exhibit the greatest relative differencesin frequency of use of the first person and passive voice, namely Education andthe Humanities (Figure 2). Three research articles (RAs) were randomly selectedfrom the most recent issue of a journal from each of these disciplines—ELTJournal and The Art Bulletin—chosen based on online availability to students
and the advice of expert informants in each discipline. However, alternative
methods of selection, such as a journal rating metric or students’ own knowledgeof the field of interest, could be used.
This analysis, performed manually by the teacher and students, revealed that
passive-voice use exceeded first-person use in all three Education RAs (Figure 3).
Conversely, first-person use exceeded passive-voice use in all three Humanities
RAs (Figure 4). These results corroborate the results of the cruder computerizedanalysis at the first stage.
Implications for the classroom
The first stage of the above analysis introduces newcomers to corpora to the tags
and metalanguage needed to perform more sophisticated corpus analyses. The
second stage introduces learners to complete texts. This stage exposes them tothe organization and constitution of texts in their fields of interest. Thus, in thecase of the example described above, students noted, unprompted, the different
writing strategies employed in the acknowledgement sections compared to the9Ferries

Figure 3. Education sample.

Figure 4. Humanities sample.
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Using Word Frequencies to Introduce Corpora, OCJSI 5, pages 5-13

other parts of academic articles. Importantly, from the point of view of datadriven learning, the second stage raises students’ awareness of how the words thatthey searched for at the first stage are used in context. This serves as a gateway
for introducing concordances once students have mastered the necessary basic
corpus skills. Concordances, and the tools used to obtain them, are considered amainstay of data-driven learning, and the simple frequency-based corpus analysis
proposed here offers one way of overcoming the obstacles to corpus use outlined
at the outset to equip students to become more active, independent learners(Chen, 2011).

Notes

1. A full list and descriptions of these and other tags used by many online
corpora, including COCA, can be found at the CLAWS part-of-speechtagger for English website. https://ucrel.lancs.ac.uk/claws
2. The disciplinary categories cited here are those used in COCA.

References

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Author bio

Jonathan Ferries is a contract lecturer at Bukkyo University in Kyoto, Japan.
f-paul@bukkyo-u.ac.jp
Received: January 20, 2023
Accepted: May 18, 2023

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