Calculating context-sensitive text correlation

Question

Suppose I want to match address records (or person names or whatever) against each other to merge records that are most likely referring to the same address. Basically, I gu

Answer 1

A good baseline, probably an impractical one in terms of its relatively high computational cost and more importantly its production of many false positive, would be generic string distance algorithms such as

• Edit distance (aka Levenshtein distance)
• Ratcliff/Obershelp

Depending on the level of accuracy required (which, BTW, should be specified both in terms of its recall and precision, i.e. generally expressing whether it is more important to miss a correlation than to falsely identify one), a home-grown process based on [some of] the following heuristics and ideas could do the trick:

• tokenize the input, i.e. see the input as an array of words rather than a string
• tokenization should also keep the line number info
• normalize the input with the use of a short dictionary of common substituions (such as "dr" at the end of a line = "drive", "Jack" = "John", "Bill" = "William"..., "W." at the begining of a line is "West" etc.
• Identify (a bit like tagging, as in POS tagging) the nature of some entities (for example ZIP Code, and Extended ZIP code, and also city
• Identify (lookup) some of these entities (for example a relative short database table can include all the Cities / town in the targeted area
• Identify (lookup) some domain-related entities (if all/many of the address deal with say folks in the legal profession, a lookup of law firm names or of federal buildings may be of help.
• Generally, put more weight on tokens that come from the last line of the address
• Put more (or less) weight on tokens with a particular entity type (ex: "Drive", "Street", "Court" should with much less than the tokens which precede them.
• Consider a modified SOUNDEX algorithm to help with normalization of

With the above in mind, implement a rule-based evaluator. Tentatively, the rules could be implemented as visitors to a tree/array-like structure where the input is parsed initially (Visitor design pattern).
The advantage of the rule-based framework, is that each heuristic is in its own function and rules can be prioritized, i.e. placing some rules early in the chain, allowing to abort the evaluation early, with some strong heuristics (eg: different City => Correlation = 0, level of confidence = 95% etc...).

An important consideration with search for correlations is the need to a priori compare every single item (here address) with every other item, hence requiring as many as 1/2 n^2 item-level comparisons. Because of this, it may be useful to store the reference items in a way where they are pre-processed (parsed, normalized...) and also to maybe have a digest/key of sort that can be used as [very rough] indicator of a possible correlation (for example a key made of the 5 digit ZIP-Code followed by the SOUNDEX value of the "primary" name).