[Corpora-List] Quantifying lexical diversity of (corpus-derived) word lists

Wed Apr 10 01:28:18 UTC 2013

I'd just like to add that you also need to hone in on which variables you'd like to analyze. Test enough of them and eventually you're bound to find something significant (this happens a lot with correlations.)

Sent from my iPhone

On Apr 9, 2013, at 7:07 PM, Jim Fidelholtz <fidelholtz at gmail.com> wrote:

> Hi, Marko,
> 
> I should start by saying that I'm a 'trained scientist' (somewhat akin to a trained seal, I suppose), with degrees in Math and Linguistics from a (we would say 'the') top university in both fields. Aside from a little Army training (using, as I recall from long ago, Hoël), I've never actually taken a course in Statistics (btw, for me the 'experts' in statistics generally are not mathematicians, but rather experimental psychologists, who have quite different foibles from 'us ['hard'] scientists', but do know their statistics). In addition, my only published use of statistics was the Chi-squared test (it was, though, appropriate for the use I gave it). 
> 
> That said (whew!), you should not think your questions are embarrassing in the slightest; they reflect what seem to me to be common concerns in the (often uninformed) use of statistics in general. My use of statistics was in the pre-computer days of the 70s (perhaps I used a slide rule, as there were no handy-dandy calculators either, or else I did it manually), but now there are easily available programs to churn out in seconds or minutes what used to take, literally, days on computers (speeds then, the fast ones, of 1 *Kilo*hertz or so; sigh). We'll soon be approaching Terahertz speeds (that's 10 to the ninth power faster!), almost certainly. One important point is to *read up on* each type of test you are considering using, especially the *limitations* on the use of each test. (For example, for the Chi Squared test, none of the cells should have over about 100 entries; otherwise, you are practically *guaranteed* statistical significance, since this test basically involves the ratio of N squared divided by N, to oversimplify.) Each statistical test has its own limitations, but the more sensitive a test is, in general, the more complicated it is to use and understand, and often its limitations will not fit what you are trying to do. (Also, it is more likely to take longer to run, but nowadays that is much less significant.) Simply put, *every* statistical test should *not* be used to analyze certain types of data, which will depend on the type of test in question. I'm sure there are handy-dandy guides on the Web in tabular form to help in choosing which test to use, and which ones to avoid for specific purposes. Likewise, some tests are more sensitive than others.
> 
> I hope these comments are of some use for you. I'm sure other list members know a lot more than I do about specific tests, and perhaps can guide you to references you can use to decide which tests to use for your purposes. Good luck.
> 
> Jim
> 
> 
> On Mon, Apr 8, 2013 at 4:33 PM, Marko, Georg (georg.marko at uni-graz.at) <georg.marko at uni-graz.at> wrote:
>> Dear corpus linguists,
>> 
>> I’m almost a tabula rasa when it comes to statistics so please excuse me if the following question is complete nonsense.
>> 
>> But there has been a problem that has been bothering me concerning the quantification of the lexical diversity (or lexical variation) in lists derived from corpora. Theoretically, these lists could be of any kind, formally or semantically defined. The idea is to compare different lists from one corpus or the same lists across different corpora with respect to how prominent the categories the lists represent are in a particular text, in a particular text type, discourse, genre, etc.
>> 
>> Token frequencies are the obvious starting point for quantifying this, assuming that if words from one list occur more often than those from another the former category will be more prominent (leaving aside the question what ‘prominence’ now means cognitively and/or socially).
>> 
>> But lexical diversity* would be another as the status of a list of two lexemes occurring 50 times each (e.g. a list of pathonyms containing ‘disease’ and ‘illness’) is probably different from one of 25 lexemes occurring 4 times each on average (e.g. a list of pathonyms containing ‘cardiovascular disease’, ‘heart disease’, ‘coronary heart disease’, ‘heart failure’, ‘myocardial infarction’, ‘tachycardia’, ‘essential hypertension’…).
>> 
>> The easiest way to quantify this would to take the number of different types/lexemes in the list. This seems fine intuitively, even though I’m not sure to what extent I should be looking for a measure that is less dependent on token frequencies (obviously, there is usually a correlation between type and token frequencies). Type-token ratios could be another candidate, but it is the converse situation, with small lists showing higher values than larger lists.
>> 
>> So I guess, my question is whether there is any (perhaps even established *embarrassment*) measure that would represent lexical diversity better.
>> 
>> Maybe it all depends on what I mean by lexical diversity and by clarifying this I would avoid the problem at the other end of the analysis. However, if anyone knows, I would be grateful to learn.
>> 
>> Thank you
>> 
>> Best regards
>> 
>> 
>> 
>> Georg Marko
>> 
>> 
>> 
>> *There is a relation to the concept of “overlexicalization” or “overwording” used in Critical Discourse Analysis, which assumes that the use of many different lexemes for the same concept, similar or related concepts points to a certain preoccupation with an idea or set of ideas. The problem here is of course ‘over’ and the question of an implicitly assumed standard of lexicalization.
>> 
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> 
> 
> 
> -- 
> James L. Fidelholtz
> Posgrado en Ciencias del Lenguaje
> Instituto de Ciencias Sociales y Humanidades
> Benemérita Universidad Autónoma de Puebla, MÉXICO
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