This is a recognized problem and is called Word Sense Disambiguation. It's hard but not intractable. One issue is that such sentences are themselves ambiguous and even human readers may disagree on their meaning. A statistical system can make a guess based on a large corpus of word-context pairs, which can approximate what a human does when attempting to disambiguate the meaning. It won't be perfect, but again, part of this is due to the fact that the sentence as it stands alone is insufficient.
Presumably, such sentences would be contained in a paragraph that would provide additional clues as to whom the word 'they' refers. Given additional context, you could then ask, "were the protesters or the councilmen fearing violence?" Document summarization and fact extraction systems could then approximate humans in such a task.
What's interesting is that word sense ambiguity underlies a lot of comedy. For instance, "Time flies like an arrow; fruit flies like a banana." The close juxtaposition of the word "like" being used in two different contexts is what makes this sentence "funny". I think it's not too far off to say that we could eventually teach AI systems to recognize humor.
Presumably, such sentences would be contained in a paragraph that would provide additional clues as to whom the word 'they' refers. Given additional context, you could then ask, "were the protesters or the councilmen fearing violence?" Document summarization and fact extraction systems could then approximate humans in such a task.
What's interesting is that word sense ambiguity underlies a lot of comedy. For instance, "Time flies like an arrow; fruit flies like a banana." The close juxtaposition of the word "like" being used in two different contexts is what makes this sentence "funny". I think it's not too far off to say that we could eventually teach AI systems to recognize humor.