Rozhovor so Sanderom Lestradeom o jeho riešení záhady Zipfovho zákona

Interview with Sander Lestrade about his solution to a century-old linguistic problem

Zipf´s Law is known for a long time. It resisted the explanation for almost a century and became the biggest mystery in computational linguistics. Recently, the media reported that Sander Lestrade from Radboud University of Nijmegen came to a solution. In a brief interview he explains his solution to the Zipf´s Law.

1. Could you, please, shortly describe Zipf's law to our readers?

Sander Lestrade: Zipf's law states that the frequency of a word in a text can be described in terms of its frequency rank such that the second most frequently used item is half as frequent as the first (frequency first item/2), the third word has one third of the frequency of the frequency of the first item (frequency first/3), etc. All the way down to the least used word that appears only once!

2. Does every language follow Zipf's law? If not, which languages do not follow Zipf's law?

Sander Lestrade: Although I haven't checked it myself, linguists say the law holds for every language indeed. (I'd predict that it does not hold for pidgin languages, however, as these do not have a proper grammar.)

3. Could you, please, explain to us your discovery? Citing from announcement "If you multiply the differences in meaning within word classes, with the need for every word class, you find a magnificent Zipfian distribution." Could you, please, explain it a bit closer, what is the difference in meaning, how do you quantify it? Maybe an example will help.

Sander Lestrade: I can imagine that this sounds a bit opaque like this;) The idea is that words differ in meaning specification: "car" is more general than "SUV" but more specific than "vehicle". You can model this in terms of a number of (abstract numerical) meaning dimensions for which words are specified. The less dimensions a word is specified for, the more referents it could apply to in principle (for anything is a "thing"). But in order to be used successfully, words have to be specific enough to single out their referents in context. You can simulate this by generating contexts with a target referent and a number of distractor objects, then see which words are specific enough to identify the target, and randomly select one of these. But you can also calculate the probability with which words will be used, given their meaning specifications.Thus, it can be shown that the most frequent words are of moderate specification, which squares nicely with the frequency of use we find for words in natural language (by checking the correlation between the frequency of use of words with their position in word taxonomies such as WordNet). This is the meaning part: words have to be general in meaning in order to be considered frequently, but specific enough to be used indeed. And in computational models, the probability of use of words can be calculated exactly.

This semantic probability should be multiplied (literally) with the need for a word of that category. Languages have rules that say how words should be combined. A verb requires one or two noun phrases (or pronouns), a noun phrase generally comes with an article, etc. This boils down to a number of word classes (such as verbs, nouns, pronouns, prepositions) that all have an expected frequency of use in a language. Roughly, the classes are used equally often, but they differ in size hugely: there are only three articles in English, but tens of thousands of nouns. A a result, an article will be used on average much more frequently than a noun.

Given what what was just said about meaning, words are not used equally frequently within their class, however. This depends on their meaning specification.

4. Does your explanation-theory tell us some insight why are languages build in this way? Why they have Zipfian distribution and not some other distribution?

Sander Lestrade: Given word classes that differ in class size by orders or magnitude, some very rough power-law like distribution could be expected. The question then is why do languages have small grammatical and huge lexical classes. The lexical classes are easily explained: we need many words to talk about the things that interest us. Why grammatical classes develop is less clear. In my thinking, they are accidental by-products of language use that only develop over time, not an inherent part of language. But not everyone would agree with this;)

Komentáre (2)
Len hádam: Ešte by mohli overiť, či aj alzheimerovci takto strácajú postupne slová, teda, či existuje vetva symetrická podľa osi Y, no a potom to pripísať exponenicálnemu rastu/poklesu spojení v mozgu.
A teraz by ste mohli urobit jednoduchu analyzu textu samotneho clanku, ci to plati :)
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