One of the properties of every known human language is that they are ambiguous. Being “ambiguous” means that something can have more than one interpretation. Humans are so good at “resolving” ambiguities (i.e., figuring out the intended interpretation) that we rarely notice them, but in fact almost everything that you will hear/read or say/write today will be ambiguous in some way or another.
Humans are indeed quite good at resolving ambiguities. If you want to get a computer program to do anything whatsoever with language, though, you have to give it the ability to deal with ambiguity–computer programs are just as incapable of ignoring ambiguity as humans are capable of resolving it. So, one of my standard exercises for students in natural language processing (treatment of language by computers) courses is to have them go through some texts and find the ambiguities. I typically have them do that with cartoons, since their humor is often based on playing with ambiguities. Tomorrow, though, I’ll be teaching at the EUROLAN “summer school” on biomedical natural language processing, so I feel obligated to give the students a biomedical example. Here’s what it’ll be. It’s a text that would be completely typical in a health record (but it is not from an actual patient). I read through it until I found 10 ambiguities, and then stopped–so, you should be able to find at least 10 points of ambiguity here–in just the first two sentences:
CLINICAL HISTORY: This prolonged video/EEG was performed on a 17 year and 4 month-old female. This study was done to completion of Phase I surgical evaluation
TECHNICAL SUMMARY: The patient underwent…
Now, if you’re a normal human, you will not, in fact, be able to find 10 ambiguities in this text–we just don’t notice them, for the most part. And that, in fact, is the point of the exercise. I’ll follow the exercise with an illustration of those 10 points of ambiguity, many–or most–of which the students won’t have noticed. Their computer programs, though–their computer programs won’t be able to miss them, and it’s their very ubiquity that beginning researchers need to have pounded into their heads.
See how many you can come up with, and then watch this space for the (or, at least, some) answers!