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Some Musings on the Ambiguity of Language
by Dr. Emanuel Paparella
2014-02-10 11:08:44
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If you don’t believe that language is ambiguous by its own nature, take a look at the Ovi magazine regular cartoons titled “Bezerk Alert,” and that will probably change your mind. The humor in those cartoons consists in the portrayal of language’s ambiguity, in words that have multiple meanings. One can only get the correct meaning of certain words and expressions by placing them in context; when placed out of context they become funny.


The Semiotician Umberto Eco

There have been various attempts to artificially create a language which is more precise, resembling the rigorous logic of mathematics; they all came to nothing. That sort of language may be ok for computers but it ill fits beings who live in a reality which is itself ambiguous. Even the concept of truth (aletheia in Greek) seems shrouded in ambiguity since truth in that language means unveiling. Unveiling from what? From ambiguity perhaps? And what happens when ambiguity is removed and the certainty of science takes its place? Logical positivism? Umberto Eco used to say that Truth is a shy maiden and does not like to be contemplated naked. This issue has been dealt and debated rather extensively in the Ovi symposium as the struggle between positivism and the liberal arts, the so called two cultures.


Be that as it may, this preamble leads to the question: how and why did language evolve? Linguists have debated this question for decades. Many prominent linguists, including MIT’s Noam Chomsky, have argued that language is, in fact, poorly designed for precise communication. Such a use, they say, is merely a byproduct of a system that probably evolved for other reasons — perhaps for structuring our own private thoughts. The fact is that every primitive society possesses language. When it does not one cannot be sure if what is being observed are humans or chimpanzees. It would appear that language humanizes us. Without it we’d be a the cognitive level of a gorilla or chimpanzee who at best can lean 300 sign language words and can hardly put together a grammar or a syntax.


Prof. Noam Chomsky of MIT

Moreover, language invariably (especially at its origins) points to a culture from which it sprang. For example, the ancient Greek language is perfectly suited to the philosophical enterprise. So, which came first: the culture or the language?

Surprising even for a Chomsky is the fact that some existing languages do not have the concept of numbers or the verb to be which seem to be universal and an essential component of any description of identity and being and existence in the Universe. Without the verb “to be” Heidegger could not have written his famous Being and Time.

In any case, language remains ambiguous. Within a system which is presently mostly utilitarian and pragmatic designed for conveying information between a speaker and a listener, each word would have just one precise meaning, eliminating any chance of confusion or misunderstanding. Now, a group of MIT cognitive scientists has turned this idea on its head. In a new theory, they claim that ambiguity actually makes language more efficient, by allowing for the reuse of short, efficient sounds that listeners can easily disambiguate with the help of context. One such linguist is Ted Gibson, a professor of cognitive science who is convinced that ambiguity should be seen in a positive light; because of ambiguity one can reuse words in different contexts over and over again.

Let us consider the word “mean.” It can mean, of course, to indicate or signify, but it can also refer to an intention or purpose (“I meant to go to the store”); something offensive or nasty; or the mathematical average of a set of numbers. Adding an ‘s’ introduces even more potential definitions: an instrument or method (“a means to an end”), or financial resources (“to live within one’s means”). But virtually no speaker of English gets confused when he or she hears the word “mean.” That’s because the different senses of the word occur in such different contexts as to allow listeners to infer its meaning nearly automatically.

Given the disambiguating power of context, the researchers hypothesized that languages might harness ambiguity to reuse words — most likely, the easiest words for language processing systems. Building on observation and previous studies, they posited that words with fewer syllables, high frequency and the simplest pronunciations should have the most meanings.

To test this prediction the MIT professors carried out investigative studies of English, Dutch and German. By comparing certain properties of words to their numbers of meanings, the researchers confirmed their suspicion that shorter, more frequent words, as well as those that conform to the language’s typical sound patterns, are most likely to be ambiguous — trends that were statistically significant in all three languages.

To understand why ambiguity makes a language more efficient rather than less so, think about the competing desires of the speaker and the listener. The speaker is interested in conveying as much as possible with the fewest possible words, while the listener is aiming to get a complete and specific understanding of what the speaker is trying to say. But as the researchers write, it is “cognitively cheaper” to have the listener infer certain things from the context than to have the speaker spend time on longer and more complicated utterances. The result is a system that skews toward ambiguity, reusing the “easiest” words. Once context is considered, it’s clear that ambiguity is actually something you would want in the communication system.

The fact that rather than remove ambiguity most natural languages retain their massive ambiguity, there are multiple ways to parse strings of words. Here a further question arises: that kind of ambiguity is actually functional for communicative purposes? It is a fact that in the US the influence of computer science in linguistics has never been higher and those operating at the border of the two fields have decided to add the natural language processing. This however poses great challenges since while it is easy for us humans with a sophisticated cognitive mechanism to disambiguate it is quite difficult for a computer to do so. Which is to say, we are a long way from having a computer write a love sonnet to another computer.

I think it would be fair to conclude for the moment that language and its ambiguity is what humanizes us and to create a language for humans that fits a computer data and processing may ultimately mean that we have dehumanized ourselves to the point of being no longer capable of distinguishing ourselves from functioning machines. In philosophy this is the problem of reductionism and materialism. One wonders: are  the MIT linguists still able to make that distinction?

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