You could probably quite effectively do 'lossy' compression on text given enough knowledge of the language. Compress words with similar meanings into each other, automatically select variants of words based on context (plurality, etc), automatically insert punctuation, and so on.
The result would probably look like it was written by someone with an incomplete knowledge of English (or by a machine) but might end up being quite readable.
Of course, on the other hand, you can just compress the text with LZMA :)
The easiest/quickest lossy text compressions that could theoretically almost halve the size of your data is to drop capitalization (and also assuming you're using some minimal encoding that can only encode the characters you're using.) there is some semantic info that is imparted by capitals, but not much.
No, that would not cut the length in half, or anything close to it.
One form of "minimal encoding" you're describing would be to use arithmetic coding where the predictor simply assigns equal probability to each symbol actually used in the input. For example, if you encode a 1000 letter message which includes at least one instance of every letter (both upper and lower case), then you'd have 52 symbols, and your simple prediction step would simply assign 1/52 as the probability for each symbol. This will result in a constant 5.7 bits of output per symbol. So your 1000 letter message would encode at 5700 bits.
Now, suppose you discard capitalization. Now there are 26 symbols, each assigned a probability of 1/26, resulting in a constant 4.7 bits per symbol. Your 1000 letter message will now encode at 4700 bits. So you only save about 17% in this very generously constructed case.
The mistake you've made is to assume that cutting the number of symbols in half will cut the symbol length in half. In fact, reducing the number of symbols by half merely reduces the symbol length by a single bit. For example, you need 8 bits to specify between 256 possibilities, but if you cut the possibilities down to 128, you still need 7 bits.
In addition, lossy compression is usually combined with lossless compression, and a smart predictor could use context to reduce the cost of capital letters to much less than one bit per symbol (assuming that the text has a predictable structure, like human language).
it's comomn kdownlege in inoitrmfaon thoery ciecrls taht you can reoerdr lttres in wrods and the relsut is pecfretly undeastnrdlabe as lnog as you keep the fsirt and lsat letrtes the smae.
I've written this page, to try to explain the science behind this meme. There are elements of truth in this, but also some things which scientists studying the psychology of language (psycholinguists) know to be incorrect.
The page also contains a number of counter-examples, show that the result often is not "pflteecry uaaedlbdrsntne".
But such features could be exploited to make lossy compression. If humans can recognize words with some of the letters randomized, then there is no need to store those letters individually. Instead just insert random letters at decomperssion phase.
The result would probably look like it was written by someone with an incomplete knowledge of English (or by a machine) but might end up being quite readable.
Of course, on the other hand, you can just compress the text with LZMA :)