Teaching: 15 min
Exercises: 10 min
  • What are collocates?

  • How do collocates work in AntConc?

  • What conclusions can be reached based on collocation data?

  • Learn how collocates work in AntConc

This is an archived version of the training module based on the dataset (a single corpus of around 1.2 million words seperated into parts containing roughly 100,000 words each).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*

Introducing Collocates

The collocates of a word are those words that tend to occur in proximity to that word more than they occur in proximity to all other words in the corpus. The idea of collocation is implemented using a variety of different statistics to determine the co-occurrence of words.

Click on the Collocates tab, enter the string “behind” in the search box, ensure that Words is ticked and Case is unticked, change Sort by to Sort by Freq, then press Start. After a little time an error will pop up.

AntConc tab interdependencies

  • Outputs in the Collocates tab are based on data generated by the Word List tab, so both need to have the same Tool Preferences for the Collocates tab to work properly.
  • In this case, go to Tool Preferences, untick Treat all data as lowercase, hit Apply, and then hit Start again.
  • The moral here then is that in AntConc any search needs to be undertaken with care. For Collocates in particular, you need to know exactly what you’ve searched for in order to read the statistical output.

AntConc then presents a slightly confusing screen. It contains the following information:

Reading Collocates

Browsing this we can start to make some observations, building on similar themes from episodes five and six. We see many commons words (“the”, “a”, “is”, “and”). We see that people (“him”, “his”, “her”) and actions (“stands”, “says”, “holds”) are related to spatial term. And we see a long tail of vocabulary, 5239 of the 8560 unique words (or about 3 in 5) occur only once in proximity to the word “behind’.

Now edit the From.. and To.. settings to 1L and 1R respectively, and hit Start again. A few things stand out:

This output tells us something about both language use and the subject of the cataloguing.

Reading stat values

Task 1: What might the stat value signify?

  • Note: to solve this problem, start by going to the Collocates tab for the string “behind” (Words ticked, Case unticked, From.. and To.. settings to 1L and 1R respectively) and observe the stat column. Note that the values around 0.5 and below (and even in negative!) are words like “a”, “and”, “left”: words that we know are common in the corpus. Note also that the higher stat values are for those words that have jumped up the list since we moved from 5L/5R to 1L/1R (“them”, “just”, “immediately”).


  • The stat value signifies the unusually high or low occurrence of words near the target word, compared to the occurance of those words in the corpus as a whole. So, there are fewer occurrences of “a” 1L/1R of “behind” than we would expect given the frequency of “a” in the corpus, and a greater number of occurances of “them” or “just” 1L/1R of “behind” than we would expect given the frequency of “them” and “just” in the corpus
    • Note: by default, the Stat column records a ‘Mutual Information’ score, which is a measure of the probability that the collocate and key word occur near to each other, relative to how many times they each occur in total.

Now we have a better sense of what Stat is doing, change Sort by to Sort by Stat and hit Start. All the top 250 or so ranked works are now those that occur only once or twice 1L/1R of “behind”, and that - as a result - have high stat scores. This isn’t very useful. To work more effectively with Sort by Stat, change the Min. Collocate Frequency field to “10” and hit Start. We now have sensible results - “immediately”, “them”, and “Just” pop to the top, and by browsing the list we can continue to make inferences about both the language used in cataloguing and the subject of that cataloguing:

From collocation to curatorial voice

The Collocates tab enables you to create a statistical overview of a corpus. But small changes to the variables in the Collocates tab can significantly change the statistics that are produced. The tool then needs to be used with caution.

Task 2: How regularised are the descriptions of clothing, accessories, and body adornments?

  • Note: to solve this problem, start by searching in the Collocates tab for wear|wears|wearing|wore (with Words ticked, Case unticked). You may need to adjust the other settings to capture the ways that words for clothing (e.g. “hat”) are used in proximity to the verb “to wear”.


  1. There is no one way of examining this problem. One approach is to edit your From.. and To.. settings to 4L and 4R respectively, chose the Sort by Stat option, and set the Min. Collocate Frequency field to “25”.
  2. Hit Start. Note that even at this frequency, some very specialised language is present: “biretta” (a type of hat worn by Roman Catholic clergy), “bicorne” (a military hat associated with Napoleon), and “rouges” (presumably with “bonnet” to refer to a type of French revolutionary hat). This indicates that precision was important to the cataloguer.
  3. In terms of controlled vocabulary there is one prominent example: 100 occurances of “spectacles” in the output (48 “spectacles” and 52 “spectacles,”), compared with zero occurances of “glasses”.
    • Note: this suggests a use case for corpus linguistics in the review of catalogue data, because tools like those in AntConc’s concordance tab can indicate historically specific cataloguing choices that may have implications for contemporary user experience for catalogue data.

Key Points

  • The collocates of a word are those words that tend to occur in proximity to that word more than they occur in proximity to all other words in the corpus