Against the Capitol I met a lion, Who glared upon me, and went surly by

It is, I believe, generally accepted that who can be used to refer to animals (or at least some animals, or at least under certain conditions). Thus, Merriam-Webster’s online dictionary has this entry (I’ve outlined the key part in red):


However, Merriam-Webster’s online learner’s dictionary has this entry:


No mentions that who can be used for animals. And it isn’t only MW. I checked six learner’s dictionaries and none of them said this was acceptable, or an option. This isn’t a huge deal, necessarily, but it can lead to confusion if, say, this has been taught as a ‘rule’ and then students read graded readers where the ‘rule’ is broken without any consequence (search for horse who, fish who, or monkey who in the Lextutor graded reader corpus, for example). Of course, there are tons of sources where students might encounter who being used for animals.

This came up because a student of mine had written “I don’t want a dog who is so big” and a peer suggested it should be which. And that’s FINE. It can be which :-). Or that :-). But it can also be who :-D.

For teachers who like to do consciousness raising or language awareness activities, this kind of situation provides opportunities for discussions of things like what if you read/hear some language in real life that doesn’t seem to match what the dictionary/grammar guide says, analyzing  lines of ‘controversial’ lexicogrammar patterns, or formulating ideas about why people choose to use who, or which, or maybe that in different circumstances (does it seem ok for certain animals but not others? is it special for pets? does it change the meaning/tone/nuance?).

Of course, the underlying ideas could be applied to other language questions, too. So, in general, the ‘corpus lesson’ here is that corpora can be used to explore alternatives to more conventional patterns and aid in developing greater language awareness. Corpus-use can be applied to not just learning frequent or common patterns of expression, but to expanding the ways in which learners are able to express themselves.

While talking about this with another teacher, it was suggested that maybe the learner’s dictionaries (and perhaps some other learner-oriented materials) don’t acknowledge who for animals as acceptable because it’s new (recent) and thus ‘non-standard’. But I have trouble seeing which of these would be considered ‘non-standard’ (in fact, I doubt that in many cases fluent English users would even notice this usage unless it were pointed out or they were looking for it). And it’s not really a recent thing, is it?:

Against the Capitol I met a lion,

Who glared upon me, and went surly by

Julius Caesar, 1.3 20-21

This corpus-based analysis (Gilquin & Jacobs, 2006) of who being used with animals may also be of interest.


Another SkELL Update

SkELL has another new feature.

In the WordSketch function, there is now a button that provides more context for the words co-occuring with the target word/lemma.

Automatic PoS tagging in the corpus sometimes results in errors, and this feature is meant to help with this problem. It doesn’t really prevent the errors, but it should help users make correct identifications despite tagging errors.

For more info, see this post from the FB CorpusCall group and play around with this example page from SkELL.

SkELL Update

I use SkELL quite often, so I was glad to read that there has been an update to the example sentences that get shown. This was an occasional, irritating issue because the kinds of SkELL-assisted activities I usually do with my students are hampered by spelling mistakes and such. For them, the learners, the cleaner data should be beneficial.

The update was announced by James Thomas on the CorpusCALL FB group, the text is reproduced in italics here:

“SKELL If you are a user of SKELL, you might have noticed a recent improvement in the quality of the example sentences. This is thanks to the deletion of sentences that contained spelling mistakes and hapax legomena. While both of these things can be of interest, it is better that the 40 example sentences of a word or phrase are as accurate as possible.

There are 10,370 instances of the word ‘dolphin’, for example, in the full corpus. The algorithm that chooses the best 40 for learners now works with cleaner data.

It’s a nice improvement. Thanks, Vit.”

**Link to SkELL**

**Earlier posts on using SkELL**

SkELL: Homonymy and Polysemy

One drawback when using SkELL is that it won’t differentiate between, say, lead/lead or the various senses of ‘rat’. The word sketch function will differentiate between parts of speech, but the easy-to-read concordance lines initially generated will have the various words, meanings, and senses jumbled up. However, this drawback can be exploited for the teaching of various kinds of homonyms and polysemous words.

There are several ways to do this, but I’ll only discuss one basic approach here. Take the word ‘sweet’. Maybe you have students familiar with the taste sense of the word, as in “The berries are rather sweet and juicy”. You could show them (or have them look up) the SkELL concordance lines for ‘sweet’. Have them mark off the sentences that they recognize as referring to sweet taste. This would leave several sentences that use ‘sweet’ in different senses, and your students could discuss what ‘sweet’ might mean in those other sentences.

skell sweet
Screenshot: Partial SkELL output for ‘sweet’

In the screenshot above, for instance, lines 9, 10, 19, and 20 appear to be describing something about people’s personalities. Discuss with your students what it could mean to describe a person as ‘sweet’.

Alternatively, students could use a dictionary to look up all/several of the senses of ‘sweet’, and then try to categorize the SkELL sentences according to each sense.

Regardless of how exactly you approach it, there are a lot of ways to exploit this drawback for teaching and learning purposes.

Any other SkELL tips?

SkELL: Easy to use for teachers and students


In a previous post I said that presentation and design factors were barriers to corpus use by teachers. I’ll add the sense that reading concordance lines is not intuitive for most people and, although central to corpus methods, adds to a discouraging visual aspect of many concordancers. Teachers don’t want to deal with this and they especially don’t want to expose their students to it. Aren’t there some tools that don’t have such a steep learning curve, have simple menus, and won’t scare our students? Thankfully, yes there are.

One of the best tools for non-specialists (teachers and students) is the Sketch Engine for Language Learning (SkELL). Among its user friendly features are a simple search mechanism (just input a word or phrase), a limited number of numbered output lines (40 max), lines in sentence format (not cut-off at a certain number of tokens before/after the node word), and plenty of white space which makes the appearance easier to read and process on screen. I’ll just go over a few straightforward ways to use SkELL.

Screenshot: SkELL’s simple menu and search

At a basic (and basic is good) level concordance lines can be used as illustrative examples of target features, lexical and grammatical. SkELL is an excellent resource for finding authentic sentences for the target word(s). One thing to keep in mind when selecting lines to use as examples is to consider what exactly you want the example for. Is it to help students understand the meaning of a word/phrase? Is it to help them understand the usage? Both? Some other skill or aspect you’re teaching? For a deeper discussion of this topic: a series of articles that discuss example sentences (particularly in dictionaries) that help learners with decoding (meaning) or encoding (usage) is included at the bottom of this post. The same principles apply to teachers wanting to use example sentences in class.

As an example, here is a screenshot showing some of the lines generated by searching for “aware”. I have outlined a sentence useful for decoding in red, and a couple sentences useful for encoding in blue.

Screenshot: some concordance lines for ‘aware’

The line “They are well informed and politically aware” is useful for decoding because of contextual clues, like ‘well informed’, which can help someone understand what ‘aware’ means. The lines “Ensure students are aware of their responsibility” and “You are probably already aware of this” are useful for encoding because they illustrate certain collocational and colligational features, such as ‘aware + of’, the high frequency use of be-verbs preceding ‘aware’, and in the latter case the use of verb + adverb preceding ‘aware’.

SkELL is also a great resource for discovering and exploring collocates. By clicking on the Word Sketch button in the top menu, a table of collocates is displayed, with the collocates separated into groups according to kinds of collocates.

Screenshot: word sketch for ‘aware’

Each collocate can be clicked, which will result in a new list of example sentences featuring the original search term and the selected collocate. This is useful for teachers, and for students to get some direct experience using a relatively straightforward, easy-to-use corpus resource.

Screenshot: several lines for ‘aware’ and its collocate ‘grow’

A few other ideas for using SkELL, though I won’t go into detail here, are creating gap-fill exercises, having students find and investigate examples/collocates (maybe each student/group of students could find and present examples of different kinds of collocates for a target word), using the sentences for translation exercises, etc.

My final point about SkELL’s usefulness is that it is a mobile friendly site, so even if students don’t have access to a PC in class, if they have smartphones or some other mobile device, they can use it just as well.

Have any other tips or good ideas for using SkELL? Please write a comment.


A second post of teaching-with-SkELL ideas is available.

Frankenberg-Garcia (2012) “Learners’ use of corpus examples”. International Journal of Lexicography, vol 25/3, 273-296.

Frankenberg-Garcia (2014) “The use of corpus examples for language comprehension and production”. Special Issue on Researching Uses of Corpora for Language Teaching and Learning, ReCALL, 26, 128-146.

Frankenberg-Garcia, A. (2015) “Dictionaries and encoding examples to support language production”. International Journal of Lexicography, 24/4, 490-512.

Each of the above articles is available through the author’s personal site.

BAISA, Vít a Vít SUCHOMEL. SkELL: Web Interface for English Language Learning. In Eighth Workshop on Recent Advances in Slavonic Natural Language Processing. Brno: Tribun EU, 2014, pp. 63-70, 8 p. ISSN 2336-4289. (online)