Matching Data Text of Two Place with Exception
Rule of thumb: if a human with a lot of time wouldn't be able to do it, there's little chance a machine could. In this case if you ask somebody to classify whether a place is a park or not, they just can't do it correctly if the place name doesn't contain any indication that it is a park.
In a case like this you would need to use external resources in order to add indications about the place. For example you could search the place name in Wikipedia, and if Wikipedia says that it's a park then you add a boolean feature &quot;true&quot; for the place.
More generally I suspect that your approach is not very solid: you looked at a few results and noticed that your matching system makes mistakes about parks. If you look at more results there's a good chance that you will find more mistakes about various kinds of cases. Fuzzy matching works only when the strings are quite similar, so it's unavoidable that the system will make mistakes one way or the other. Even if you try to fix every kind of mistake one by one with some ad hoc exception, the system will never be completely correct. Importantly, this would make the system completely specific to the dataset, so it wouldn't generalize well to new instances.
This is an issue related to task specification and design: the automatic system can only achieve a certain level of quality, so maybe the task should be framed differently. For example, the system could be in charge of finding candidate pairs for matching, and a human expert would annotate the actual matches among the candidates. Or you could just decide to live with the errors, if they are not critical for the application.