Tools to Improve Academic Writing

Sarah Deutchman, Waseda University

Writing for publications can be daunting for novice writers as it involves joining a new discourse community. Because each academic community is shaped by its own manner of recording information and communicating knowledge (Cotos, 2018), it is necessary to learn the best ways to present work based on the expectations of the academic community and its publications (Hyland & Jiang, 2018). Thus, to publish academically it is important to master a manner of writing and sequences of words (i.e., formulaic expressions) to match what experts look for (Hyland, 2008). These word sequences can be referred to as lexical bundles, chunks, or clusters. Examples of lexical bundles include in this paper we will discuss, due to the fact that, and as can be seen. Mastering this language effectively is imperative because not using the correct register may preclude the publication and communication of important research (Englander, 2006, as cited in Hyland, 2019). Free online language resources are useful tools to aid the selection of phrases for effective communication. This article will introduce a few of these resources and explain how to use them.

Academic Phrasebank

The Academic Phrasebank ( lists phrases according to their functions (e.g., describing trends, defining terms). It is a free online resource from the University of Manchester created by John Morley that helps users understand how and when a particular phrase is used. An example can be seen in Figure 1 which shows how to define a difficult term.


Figure 1

Phrases Showing How to Define Difficult Terms

Sketch Engine for Language Learning (SKELL)

SKELL ( is a tool that can be used to look for patterns in text. By combining SKELL with the Academic Phrasebank, a better picture of how phrases are used in context can be obtained. Figure 2 illustrates the analysis of the phrase in accordance with the. SKELL provides examples of this queried phrase in a sentence. These examples can inform the writer of where the phrase is placed within a sentence and which words precede or follow it. Here, we can see that immediately following the phrase is a noun or an adjective.


Figure 2

Concordance Lines of “in accordance with the”


AntConc ( was created by Laurence Anthony (2022) and can be downloaded from his website. The software can be used to create a corpus of texts that can be used to identify patterns. The results in Figure 3 were obtained by first uploading several reference articles used to write this paper into AntConc. A search of the word learner was then carried out to find clusters. This tool can sort words by frequency and show them in context within the imported works. This technique can be useful for analyzing a journal or resource to see how certain words or phrases are used. Furthermore, Anthony (2022) posts videos on YouTube where he explains how to use the software in detail.


Figure 3

AntConc Cluster Results for “learner”

Vocabulary Profilers

Vocabulary profilers comprise a set of tools that can analyze writing to categorize vocabulary into frequency levels. The data in Table 1 was taken from the vocabulary profiler available on the EAPFoundation website (, where there are different options for analyzing a text. Table 1 shows the results of analyzing an abstract with the New General Service List (NGSL) and the New Academic Word List (NAWL). This type of profile can show the NGSL level of words and if those words are on the NAWL. The NGSL shows more common words, while the NAWL features more academic vocabulary. The resulting analysis indicates that the abstract contains mostly high-frequency words. A writer can use this approach to determine the academic level of particular words and make choices more suitable to their needs.


Table 1

Analysis of an Abstract at the EAPFoundation Using the NGSL and NAWL

Words and Phrases

Words and Phrases is another vocabulary profiler, one that has been incorporated into COCA, the Corpus of Contemporary American English ( The sample in Figure 4 shows how this tool makes it possible to determine vocabulary levels within a text. Low-frequency words are considered more academic than mid- or high-frequency ones. The Word function displays the genre, collocations, lexical bundles, synonyms, and concordance lines.


Figure 4

Analysis of an Abstract with Words and Phrases

In this sample, most of the words used are not considered academic because they are high-frequency. With this knowledge, an author can now choose to replace these items with more academic synonyms or analogous phrases.

Word profilers can also be used to determine the vocabulary level used in a particular journal in order to gauge acceptable levels of academic word usage. Figure 5 shows part of the results of using the Word function in Words and Phrases to look up administrate. The collocates usually paired with this word are displayed, along with links to other websites where the word can be heard as part of a short video clip. Links to Japanese translations are also provided.


Figure 5

Information on “administrate” Using Words and Phrases


Academic Word Suggestion Machine (AWSuM)

Swales’s view of genre theory (as cited in Cotos, 2018) was built on the moves and organization used to convey a message in ways preferred by a particular discourse community. In genre theory, text is organized into several moves (Cortes, 2013). These moves can also be separated into different steps. For example, Table 2 features the framework used for tagging moves in an abstract, which was taken from Mizumoto et al. (2017). The first move of an abstract is the Introduction, the first step of which could be Arguing for prominence. Subsequent moves are Presenting the research, Describing the methodology, Summarizing the findings, and Discussing the research. New web-based tools such as AWSuM ( have been built upon this theory. AWSuM contains an autocomplete feature that suggests the most frequent lexical bundles in a move within a particular sample of text (Mizumoto et al., 2017). As a result, AWSuM can suggest phrases based on what part of the article is being written and the writer’s intention.


Table 2

Move Framework Used to Create AWSuM


Final Thoughts

Technology has made it much easier to notice patterns most used in academic writing and isolate them for deeper reflection. These phrases are ones that experts often expect to see when reviewing an article. Corpora such as SKELL and COCA show how particular phrases are used in context. AWSuM supplies suggestions for the deeper structures of each section of a paper. The tools mentioned in this article are just a small sample of what is freely available online. Novice writers are encouraged to make use of these resources to improve their chances for publication.



Anthony, L. (2022). AntConc (Version 4.0.4) [Computer Software]. Tokyo, Japan: Waseda University.

Cotos, E. (2018). Move analysis. In C.A. Chapelle (Ed.), The concise encyclopedia of applied linguistics. John Wiley & Sons.

Cortes, V. (2013). The purpose of this study is to: Connecting lexical bundles and moves in research article introductions. Journal of English for Academic Purposes, 12(1), 33–43. 

Hyland, K. (2008). As can be seen: Lexical bundles and disciplinary variation. English for Specific Purposes27(1), 4-21.

Hyland, K. (2019). Participation in publishing: The demoralizing discourse of disadvantage. In P. Habibie & K. Hyland (Eds.), Novice writers and scholarly publication (pp. 13–33). Palgrave Macmillan, Cham.

Hyland, K., & Jiang, F. K. (2018). “In this paper we suggest”: Changing patterns of disciplinary metadiscourse. English for Specific Purposes51, 18–30.

Mizumoto, A., Hamatani, S., & Imao, Y. (2017). Applying the bundle–move connection approach to the development of an online writing support tool for research articles. Language Learning67(4), 885–921.


Sarah Miyoshi Deutchman teaches at Waseda University as a part-time lecturer. She has taught English for over 14 years in three different countries. She has been teaching at the university level for four years. Her areas of research include data-driven learning, corpus linguistics, and vocabulary. Email: