Searching concordances


Teaching: 15 min
Exercises: 20 min
  • How can I search in AntConc?

  • How can I use search to discover features of catalogue data?

  • Explain how to search a concordance

  • Explain how to read a concordance

This is an archived version of the training module based on the dataset (a single corpus of around 1.2 million words seperated into parts containing roughly 100,000 words each). For information on how we processed the .txt files in for use in AntConc, see *Creation of the BMSatire Descriptions corpus*

Searching in AntConc

After generating lists that characterise a whole corpus, the other main way to interact with a dataset in AntConc is to use search to narrow your enquiry to a subset of a corpus.

The Concordance tab is one of many tabs that responds to search. Navigate to the Concordance tab, put the string “wear” into the search box, and hit Start.

After a little thought, AntConc populates the tab. We can observe that - by default - a search in the Concordance tab does a number of things:

Comparative analysis in AntConc

By working on multiple files, and by providing outputs that identify which result relates to which file, the Concordance tab gives us a way into comparative analysis of catalogue data, be that longitudinal (files seperated by the decades in which catalogue entries were made), by collection, or be cataloguer. We discuss comparing corpora in more detail in a later episode.

The default search can be changed by use of the options available in the Condordance tab.

If you untick Words, and rerun your search, you’ll notice that AntConc returns more hits and that some of those results are for variants of the word “wear”. This does not mean, however, that you’ve instructed AntConc to look for variants of the word “wear”. Rather, you have searched for the four-character string wear, meaning that the results could include everything from real English words such as “wear”, “wears”, and “wearing” to strings that contain the character sequence wear, such as “footwear”, “12345wear”, or “jdeoakewearldsgldslg”.

Now we know how the Words option works, tick the Case option, change the search term to the string “Wear” and hit Start. Five results are returned. This is because we have made a case-sensitive search, and the only instances of the string “Wear” in the corpus are for the word ‘Wearing’ positioned at the start of a sentence (presumably, there are no people called “Wearing” named in the corpus!)

Finally for now, note the Kwic Sort section. Kwic means Keywords in Context and in AntConc this sort works on levels: first Level 1, then Level 2, then Level 3. The values to in the boxes refer to the position relative to the search term on which the sort takes places: so 1R sorts by the first word to the right of the search term, 1L by the first word to the left of the search term, 0 by the search term itself, and so on. Note that these levels correspond not only to how the concordance is sorted, but also to the colouring on the words in the concordance.

Task 1: get to know the Kwic sort

  • Search the corpus until you find a word with somewhere between 50 and 100 hits (you might want to play around with the Words and Case options to narrow or expand your search). Spending a few minutes changing the Kwic Sort to resort your output in various ways. Write down any queries you have about how the sort works and ask your instructor when the time is up.

Search in AntConc also takes wildcases, both in the form of a limited set of native wildcards, and in the form of regular expressions. We discuss using regular expressions (or regex) in AntConc in a later episode. For now, we will focus on the native wildcards, which are similar to those in regex (for those who are familiar). These are:

Using the string “wear” as an example, wildcards behave as follows (with Words and Case ticked):

Note that by turning off the Words option, AntConc will return results that contain your search string irrespective of where in the word it appears. So, for example with Words unticked (and Case ticked) wildcards behave as follows:


Having learnt using AntConc’s Concordance tab to search a corpus, work in pairs or small groups on the following challenges.

Task 2: Work out rough % of the word “he” used at the start of a sentence.

  • Note: to solve this problem, you may find it helpful to do more than one search.


  1. Search “he” and “He” separately with Words and Case both ticked. You should get 4670 hits for “he” and 5557 hits for “He”.
  2. The answer is just under half.
    • If you scroll through the results, you’ll see that this is an exact solution for this corpus. However, this is not a perfect query, as other corpora may contain typographic errors or uncommon uses of the word “he”. This is an example when knowing your corpus can help you craft a good enough query, rather than have to expend time and energy creating the perfect query. Handily, the ouputs provided by this AntConc tool are a great way of getting to know a corpus.

Task 3: Compare the use of past and present tense forms of the verb “say”. Decide which is more common, by roughly what factor, and if there anything that characterises the past tense form.

  • Note: there are three present tense forms of the word “say”. This problem can also be solved with one query.


  1. Search for said|say|says|saying (with Words ticked and Case unticked), with the Kwic sort set to Level 1 equals 0 and the other levels unticked.
  2. The word “says” is the most common. We can infer this by scrolling through the sorted results, as “says” starts roughly half way down the list and continues to the end.
  3. 1539 hits are returned, only around 70 of which are for the word “said”, meaning that present tense forms are over twenty times more common.
  4. In terms of what characterises the use of “said” in the corpus, browsing the concordance suggests two main uses: first, instances where the curator/cataloguer is speaking or interpretating (“he is said to be”, “the print is said to be”), and second, in speech transcribed from the print, which represents an error in processing the corpus given that transcribed speech should have been replaced with the placeholder *TRANSCRIBED*.
    • This is a good example of using the browsing features within AntConc to infer results. The exact answers could be computed from outputs saved to file, but in many cases reading a sorted list does the same job.

Task 4: Examine how adverbs are used to modify the verb “stand”.

  • Note: you can search for two-word strings and wildcards can be used more than once.


  1. Search for stand* Words ticked and Case unticked. You should get 7824 hits.
  2. Next, search for stand* *ly (again with Words ticked and Case unticked), with the Kwic sort set to Level 1 equals 1R and the other levels unticked. You should get 197 hits, suggesting that modifiers are used infrequently for the word “stand”.
  3. Browse through the output. Note that of the “-ly” words present, there is a small number of unique words, and that those that are more commons indicate readily identifiable states of being (“agressively” and “stiffly” are more common than “truculently” and “deprecatingly”). This potentially indicates that the cataloguer exercised relative control of their vocabulary (that is, there are few modifiers and of those a small number dominate), but this hypothesis would need to be tested against the wider corpus.

Key Points

  • You can search a corpus in AntConc using free text and wildcards

  • Carefully changing the search settings enables you to build better queries

  • In addition to generating precise data, AntConc can be used get to know a corpus and make rough suggestions as to its character