How to improve the accuracy of automatic conflict resolution?
There are many candidate approaches you could consider.  I would suggest you be creative about what features you could use, to make predictions about which candidate is more likely to be the intended one.



One approach is to use a language model for the prevalence of different phrases.  For instance, if $w, x$ are two words, define

$$r(x | w) = {P(w \; x) \over P(w) \times P(x)}.$$

(Here $P(w \; x)$ represents the prevalence of the 2-gram $w \; x$ in some large corpus, and $P(w)$ represents the prevalence of the 1-gram $w$ in the corpus.)

Now if the previous word is $w$ and the next word is either $x$ or $y$, compare $r(x | w)$ to $r(y | w)$ to see which is larger, and use that to resolve the conflict.

For example, comparing $r(\text{Tallahassee} | \text{at})$ to $r(\text{shake} | \text{at})$ using the Google n-gram viewer, we find that the former is much larger.  In particular, the former is $1.2 \times 10^{-6}/(0.3 \times 8.5 \times 10^{-5}) \approx 0.05$, while the latter is $4.8 \times 10^{-8}/(0.3 \times 9.7 \times 10^{-4}) \approx 10^{-4}$, which is much smaller.  We conclude that "Tallahassee" is much more likely to follow "at" than "shake", so the conflict should be resolved by the former.

You can generalize this to use any number of tokens of previous history (e.g., the previous 2 words instead of the previous 1 word).



Another approach would be to use something similar, but replace words with their parts of speech.

For instance, Google's n-gram viewer supports part-of-speech queries.  You can quickly see that "ADP Tallahassee" (basically, a preposition followed by Tallahassee) is more common than "ADP shake", so "I eat at" is more likely to be followed by "Tallahassee" than "shake".  In contrast, you can see that "DET shake" is more common than "DET Tallahassee"; so "a" (an article) is more likely be to be followed by "shake".