Corpus insider #4: The problem with polysemy

It's a bit of a standing joke that every talk I give includes the word polysemy, but it's such an important concept to bear in mind when you're looking at language in any context and especially for any corpus research. Recently, I gave a talk to students at Goldsmiths, University of London, about careers in linguistics. I wanted to give them a taste of both corpus research and lexicography, so I put together a small set of corpus lines for them to look at to tease out the different senses of a word and organize them into a dictionary entry.

Whilst it's possible to do a corpus search for a specific lemma (e.g. rest as a verb; rest, rests, rested, resting or rest as a noun; rest, rests), with reasonably reliable (if not 100%) results, corpus tools can't distinguish between the different senses or uses of a polysemous word. If you think about the noun rest, which sense immediately springs to mind? It's one of those words that highlights the difference between our intuitions and the realities of usage. Quite likely, the first sense you thought of was to do with 'taking a break or time to relax'. In fact, the rest (of) meaning 'what's remaining' or 'the others' is something like three times as frequent.

When lexicographers are working with a corpus to put together a dictionary entry, determining the sense division and ordering of senses is a manual process. You can get a flavour of a word by looking at its collocates (for example, using WordSketch in SketchEngine), but that only tells part of the story - you'll find the ‘relax’ sense of rest has far more strong collocates than the duller, more functional the rest of. 

Section of a WordSketch for rest (noun) - English Web 2015 via Sketch Engine

You can sort concordance lines to the left and right of the node word and you start to see the patterns emerge (here, the rest of becomes very obvious). But ultimately, you just have to go through a sample of cites manually to establish the different senses and uses (including as part of phrases), and the frequency order. The actual statistical frequency of a particular sense is almost impossible to determine in most cases, not least because, for many words, there are senses which overlap and examples that are ambiguous.

So what are the practical implications of this?

Dictionary frequency information: A number of learner’s dictionaries (Collins COBUILD, Macmillan, Longman) provide information about the frequency of a word using a system of stars or dots. Whilst this is useful in giving you a ball-park guide to more and less frequent words, the ratings are based on the frequency of the whole word, not the individual senses. For some words, all the senses may be relatively high frequency, while in other cases, the first sense(s) may be high frequency and others quite obscure.

Phrases: It is possible to find the frequency of many phrases with carefully constructed corpus searches, but phrases with variable elements and those containing very common words (such as phrasal verbs) which could co-occur in different ways are much trickier to pin down. For that reason, they’re not generally allocated their own frequency information and just get lumped in with the individual headwords.

Word lists: Many frequency-based word lists also don’t take into account the different senses of a word and their relative frequency.  Unless words on the list come with definitions attached, it’s difficult to know whether they just refer to the most frequent sense or to other senses as well.

Text analysis tools: Tools that allow you to input a text and get a breakdown of the words by frequency or as ranked in EVP, for instance, such as Text Inspector or Lextutor, will generally allocate words according to their overall frequency or most frequent sense. So, an obscure sense of a common word, such as leg in the context of a cricket match (see sense 5 here), will likely be labelled as high frequency. The paid version of Text Inspector does allow the user to choose the relevant sense of a word when looking at EVP labels from a drop-down menu, but it doesn’t offer off-list options (including the cricketing sense of leg which it just labels as A1) or allow you to allocate words to phrases that haven’t been automatically detected.

So, does this means that all these tools are completely useless? Of course not. In many cases, we’re using frequency information as a rough guide, so finer sense distinctions don’t come into play. Like anything though, it’s important to know the limitations of the sources and tools you use and to be on the look-out for anything that doesn’t seem quite right.