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Many grammarians go about their days maligning ambiguity. Don’t use while when you mean although, they say, because it’s ambiguous. Don’t use since in place of because either, they say. And so on. If they were right, then everyone would be confused by these two sentences:

(1a) Since I eat the right foods in the right combinations, I’m not focused on calorie restriction.
(1b) The Oscar-winning director tells the story of Venezuela’s “peaceful revolution” since Chavez came to power in 1998 […]

But people aren’t confused, because the clauses readily disambiguate since. (1a) uses the present habitual I eat, which prevents the “ever since” meaning from making sense. (1b) uses the past perfect, which would allow for either meaning, but there’s nothing in the sentence that a “because” clause could attach to, so the “ever since” meaning is the relevant one.

In general, these concerns about ambiguity are actually concerns about potential ambiguity, where someone intentionally misreading the sentence or not paying a lot of attention to it could misread it.* These situations usually don’t result in actual ambiguity for reasonable readers. That’s not to say there are never ambiguities, but only that these ambiguities are usually much less of a problem than prescriptivists claim.

(2) In a second term, Carter might have moved the course of government toward the left, but since Reagan won the election the nation’s political movement has been toward the right instead.

When it is important that the reader gets exactly the meaning you desire, it is important to remove ambiguity, and at those times you’d want to, for instance, replace since in (2) with because or ever since. When the distinction is either obvious or unimportant, there’s no reason to change it. And the problem is that trying to make language completely unambiguous often comes at the cost of readability and comprehension:

(3) Upon such default, and at any time thereafter, Secured Party may declare the entire balance of the indebtedness secured hereby, plus any other sums owed hereunder, immediately due and payable without demand or notice, less any refund due.

That’s legalese; an officiously precise form of the English language that is borderline incomprehensible to those not trained in its tortuous wendings. Although there is little ambiguity in (3), it’s very difficult to extract the meaning, and the sentence seems bloated. But just try shortening or clarifying the above sentence without re-introducing an ambiguity, and you’ll see the difficulty: languages are not built for precision. And, in fact, ambiguity in language is not a bug, but a feature. This is a point nicely summarized by Frederick Newmeyer in a paper that I otherwise disagree with heartily, Grammar is grammar and usage is usage (PDF):

“The transmission rate of human speech is painfully slow […] less than 100 bits per second—compared to the thousands that the personal computer on one’s desk can manage. A consequence is that speakers have to pack as much as they can into as short a time as they can, leading to most utterances being full of grammatical ambiguity […] For that reason, humans have developed complex systems of inference and implicature, conveyed meanings, and so on. […] Stephen Levinson phrased it beautifully: ‘[I]nference is cheap, articulation expensive, and thus the design requirements are for a system that maximizes inference’ (Levinson 2000:29).”

[Emphasis mine.] Ambiguity is useful, as ambiguous sentences can convey the necessary information just like unambiguous sentences, but in fewer words. The reader, listener, or whoever you’re directing your language to is then able to use their knowledge of context and implicatures to determine the appropriate interpretation (this is the “inference” process). A great example of this (again from the Newmeyer paper, but originally from Martin, Church, & Patel 1987) is (4), which has 455 possible parses, many of which yield different meanings.

(4) List the sales of products produced in 1973 with the products produced in 1972.

And yet, given a bit of context, and some knowledge of what one is trying to do in the situation in which this sentence is uttered/written, you are able to pretty quickly figure out which potential meaning is the best. Trying to make the sentence perfectly unambiguous would only drown the reader in words.

Summary: Pick your battles against ambiguity. Where ambiguity is truly detrimental, put forth the effort to clarify, to root out plausible ambiguities and remove them. Where ambiguity is tolerable, it can be better to leave it in to keep from exhausting yourself and your audience.

[If you’re interested in more on potential vs. effective ambiguity, Arnold Zwicky had a post on Language Log from 2008 discussing this topic. Now that I look at his post again, I’ve realized that most of what I said here, he already said there, plus more.]

*: I know some of you in the audience are editors, and I’ve had a few editors explain to me that their job consists in part of idiot-proofing writing. This requires you to try to make it as easy on the reader as possible, and to assume that the reader will fall into whatever garden paths and other meaning pitfalls are possible. Removing the potentially ambiguous situations might be seen as a step in this task. That’s a fair counter-point, but it does not compel a change, and the change must be weighed against the considerations. Avoiding ambiguity that requires the reader to wantonly misinterpret is less crucial than avoiding easy-to-fall-into ambiguities.

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A lot of people make claims about what "good English" is. Much of what they say is flim-flam, and this blog aims to set the record straight. Its goal is to explain the motivations behind the real grammar of English and to debunk ill-founded claims about what is grammatical and what isn't. Somehow, this was enough to garner a favorable mention in the Wall Street Journal.

About Me

I'm Gabe Doyle, currently a postdoctoral scholar in the Language and Cognition Lab at Stanford University. Before that, I got a doctorate in linguistics from UC San Diego and a bachelor's in math from Princeton.

In my research, I look at how humans manage one of their greatest learning achievements: the acquisition of language. I build computational models of how people can learn language with cognitively-general processes and as few presuppositions as possible. Currently, I'm working on models for acquiring phonology and other constraint-based aspects of cognition.

I also examine how we can use large electronic resources, such as Twitter, to learn about how we speak to each other. Some of my recent work uses Twitter to map dialect regions in the United States.

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