Azure Search - issues with Phonetic Analyzer

Question

Our clients query on our Azure Search index, mostly for people's names. We are using the Lucene analyzer for all of our fields. We build the query string by making the client's input name into a phrase, and adding proximity rate of 3. Because we search using a phrase, we can not use the Fuzzy Search capability of the Lucene analyzer, as it only works on single words.

We were therefore in search of a solution for being able to bring back results with names that weren't spelled exactly as the client input them. We came across the phonetic analyzer, and have just implemented the Metaphone algorithm into our index. We've run some tests and while it gets us closer to what we need, we still see some issues:

1. The analyzer's scope is so wide, that it's bringing back a lot of false positives. For example, when searching on Kenneth Gooden, it brings back Kenneth Cotton. That's just a little too far to be considered phonetically similar, in our opinion. Can the sensitivity be tweaked in any way, or, can something be done to boost some other parameter to remedy this?

2. When doing a search on Barry Soper, the first and highest-scored result that comes back is "Barry Spear." The second result, scored lower, is "Soper, Barry Russell." To a certain extent, I can maybe see why it's scored that way (b/c of the 2nd one being last name first) but then... not really. The 2nd result contains both exact terms within the required proximity. Maybe Azure Search gives priority to the order of words in the phrase before applying the analyzer? Still doesn't make sense to me. (Side note - this query also brings back "Barh Super" - see issue #1 above)

I would like to know if someone could offer suggestions to tweak Azure Search's behavior to work more along the lines of what we need, OR, perhaps suggest an alternative to the phonetic analyzer. We haven't tried any of the the other available phonetic algorithms either yet, only b/c it seems Metaphone is the best and most commonly-used. But we're open to suggestions regarding the other algorithms as well.

Thanks.

Answer 1

You are correct that the fuzzy operator only works on single terms. In this case, you can use a custom analyzer (phonetic tokenfilter) or Synonyms feature (in preview). I am not sure what you meant by "we have just implemented the Metaphone algorithm into our index" but there are several phonetic tokenfilters you can choose from in Azure Search custom analysis stack. Synonyms is a newer feature only available in preview, you can take a look here. For synonyms, you will need to define synonyms rules, say 'Nate, Nathan, Nathaniel' for example, and at query time, searching for one automatically includes the results for the others.

Okay, then how should I use these building blocks in a way to control relevance for my search? One way to model is to use separate field for each expansion strategy. For example, instead of a single field for the name, you can have three fields, say 'name', 'name_synonym', and 'name_phonetic'. The first field 'name' is for exact matches, 'name_synonym' field has synonyms enabled and the third uses a phonetic analyzer and broadens the search the most. You can then use the scoring profile to boost scores from matches in each field. You can give the boost value of 10 for exact matches, 5 for synonyms and 1 for phonetic expansions, for example. Your search will be issued against these three internal fields.

Regarding your question as to why 'Soper, Barry Russell' is ranked lower than 'Barry Spear'. After the phonetic analysis. the words 'soper' and 'spear' reduce to the same form both at indexing and query time and treated as if they were identical terms. In computing the score and ranking, the search engine uses analyzed form of the terms and phonetic similarity makes no influence to the score. That’s why, secondary factors, like field length, will play a more significant role influencing the relevance score.

Hope this helps. I provided one example to model this but you could also take a look at term boosting in the full lucene query syntax.

Let me know if you have any additional questions.

Nate