Phonetic Matching with Apache Solr

with Apache Solr
Markus Günther
Freelance Software Engineer / Architect
| |
Phonetic Matching
mail@mguenther.net mguenther.net @markus_guenther

Phonetic matching is concerned with searching for spelling variations in large databases.
Age-old problem
Algorithmic solutions date back to the pre-computer era
Soundex was invented by Russell and Odell in 1912
Compute a phonetic value for a given name
Names that sound the same share the same phonetic value
Variation: American Soundex
Variation: Daitch-Mokotoff (DM) Soundex
Soundex tends to generate many false hits which lowers precision
© 2022 Markus Günther IT-Beratung

American Soundex is a reasonably simple algorithm.
Rules
1. Replace the first letter of the name and drop all occurences of a, e, i, o, u ,y, h, w.
2. Replace consonants with digits as suggested by mapping table.
3. Retain only the first letter for two or more adjacent letters mapped to the same number.
4. Retain only the first letter for two letters mapped to the same number that are separated
by h, w, or y.
5. Trim the encoded numbers to a total of three. Pad with 0 if there are less than three.

American Soundex is a reasonably simple algorithm.
public class AmericanSoundex {
private static final String MAPPING = "01230120022455012623010202";
public static String encode(final String term) {
char code[] = { term.charAt(0), '0', '0', '0'};
char previousDigit = encode(code[0]);
int count = 1;
for (int i = 1; i < term.length() && count < code.length; i++) {
final char ch = term.charAt(i);
if (ch == 'H' || ch == 'W' || ch == 'Y') continue;
final char digit = encode(ch);
if (digit != '0' && digit != previousDigit) {
code[count++] = digit;
}
previousDigit = digit;
}
return String.valueOf(code);
}
}

Let's take a look at a couple of examples.
Name Phonetic value
Robert R163
Rupert R163
Rubin R150
Ashcraft A261
Ashcroft A261

American Soundex is not optimized for Eastern European names.
Name Phonetic value
Schwarzenegger S625
Shwarzenegger S625
Schwartsenegger S632
A search application would not be able to find a match with that misspelling.

Daitch-Mokotoff Soundex has a solution for this.
Name Phonetic values
Schwarzenegger 474659, 479465
Shwarzenegger 474659, 479465
Schwartsenegger 479465
Given a pair of names, we have a phonetic match if at least one of their codes match.

Soundex suffers from a focus on the anlaut for short names leading to false-positives.
Phonetic value Names
S300 Scott, Seth, Sadie, Satoya, ...
C500 Connie, Cheyenne, Conway, ...
T200 Tasha, Tessa, Tekia, ...

This isn't always the case, though.
Phonetic value Names
M622 Marcus, Marcos, Marques, Markus, Marquice, Marquisa, ...
F652 Frank, Francisco, Francis, Franklin, Francois, ...
C150 Chevonne, Chavon, Chavonne, Chivon, Cobin, ...

Beider-Morse Phonetic Matching

Instead of focusing on spelling, Beider-Morse factors in linguistic properties of a language.
Of limited interest for common nouns, adjectives, adverbs and verbs
Good strategy for proper nouns (i.e., names)
History: Started off primarily for matching surnames of Ashkenazic Jews
Example: Consider variations of Schwarz (standard German spelling)
Schwartz (alternate German spelling)
Shwartz, Shvartz, Shvarts (Anglicized spelling)
Szwarc (Polish), Szwartz (blended German-Polish)
Svarc (Hungarian), Chvartz (blended French-German)

Step 1: Identifying the language
BMPM includes about 200 rules for determining the language
Some are general, some need context
Examples Inferred Language(s)
tsch, final mann or witz German
final and initial cs or zs Hungarian
cz, cy, initial rz or wl, ... Polish
ö and ü German, Hungarian
Allows to specify a language explicitly

Step 2: Calculating the exact phonetic value
Forms of surnames used by women differ in some languages
Affects Slavic languages, Polish, Russian, Lithuanian, Latvian
Masculine endings Feminine endings
Suchy Sucha
Novikov Novikova
BMPM replaces feminine endings with masculine ones

Step 2: Calculating the exact phonetic value
1. Replace feminine endings with masculine ones.
2. Identify the exact phonetic value of all letters.
1. Transcribe letters into a phonetic alphabet.
Applies language-specific rule set in case of one possible language.
Applies generic rule set in case of multiple possible languages.
2. Apply phonetic rules that are common to many languages.
e.g. final devoicing, regressive assimilation
3. At the end, the algorithm yields the exact phonetic value.

Step 2: What do language-specific rules look like?
BMPM applies roughly 80 mapping rules for German
sch maps to S
s at the start and s between two vowels maps to z
w maps to v

Step 2: What do language-agnostic rules look like?
BMPM uses more than 300 generic rules
a final tz maps to ts
Some generic rules might be applicable to specific languages only
step 1 rules out certain languages
rule is applied if it complies with the remaining possible languages

Step 3: Calculating the approximate phonetic value
Some sounds can be interchangeable in specific contexts
beginning / end of word
previous next / letter
Language Example Sounds alike
Russian unstressed o is pronounced as a Mostov, Mastov
German n before b is close to m Grinberg, Grimberg
Spanish phonetic equivalence of n and m Grinberg, Grimberg
Rules can be language-agnostic or -specific

Step 4: Searching for matches
1. BMPM generates the exact and approximate phonetic value for a given name.
2. We have an exact match if two names match on their exact phonetic value.
This might be too aggressive for your use-case.
3. We have an approximate match if two names match on their approximate phonetic value.
Matches done by BMPM are not necessarily commutative.

Apache Solr supports a variety of phonetic matching algorithms.
Beider-Morse Phonetic Matching
Daitch-Mokotoff Soundex
Double Metaphone
Metaphone
Soundex

Refined Soundex
Caverphone
Cologne Phonetic
NYSISS

Add a field type that works with the phonetic matching algorithm.
Admissible values for ruleType are: APPROX and EXACT
They map to the semantics of approximate matches resp. exact matches
<fieldType name="phonetic_names" class="solr.TextField"
positionIncrementGap="100" autoGeneratePhraseQueries="true">
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"></tokenizer>
<filter class="solr.BeiderMorseFilterFactory"
nameType="GENERIC"
ruleType="APPROX"
concat="true"
languageSet="auto"></filter>
</analyzer>
</fieldType>

Add an index field using the resp. field type.
You probably already have a name field of sorts for basic name searches.
Use a copyField-directive to source name_phonetic from that field.
<field name="name_phonetic"
type="phonetic_names"
indexed="true"
stored="false"
multiValued="false"></field>
<copyField source="name" dest="name_phonetic"></copyField>

Execute queries against that field.
Query for mustermann
(name_phonetic:mustermann)

Let's do a couple of experiments with different parameters for BMPN.
Dataset: Large enterprise naming directory, approx. 340k individual persons
Naive implementation using phonetic matching incl. wildcards and N-Gram backed
fields yields approx. 3k results for a popular surname
Queries:
Large result set: q=(name_phonetic:meier)
Small result set: q=(name_phonetic:<some-unique-name>)

Experiment 1: Querying for a popular name
Variant ruleType languageSet q=(phonetic_name:meier)
Naive - - 2997
1 APPROX auto 1279
2 EXACT auto 1228
3 APPROX german,english 1261
4 EXACT german,english 1216
Restricting languages to pre-dominantly ones of the corpus removes non-intuitive matches
Almost no noticeable diff between APPROX and EXACT wrt. result quality

Few ordering issues, meier almost everytime before phonetic variations

Experiment 2: Querying for a unique name with spelling variations
Variant ruleType languageSet Correct Var. 1 Var. 2
Naive - - 7 0 30 (non-intuitive)
1 APPROX auto 1 5 14 (no match, not
intuitive)
2 EXACT auto 1 0 1 (no match)
3 APPROX german,english 7 (top
match)
5
(match)
25 (no match,
intuitive)
4 EXACT german,english 1 0 1 (no match)
Variant 3: Precision is good, recall could be better (i.e. one-off-corrections)

Adding one-off-corrections using Damerau-Levensthein distance complements BMPM.
Prerequisites
name index field that stores <first name> <middle-initial> <surname>
name index field uses n-grams
Refine the query
Can be applied within phrases as well to allow for displacements
"Mustermann Max" should yield the same results as "Max Mustermann"
(name_phonetic:mustermann) OR (name:mustermann~1)

Adding a boost on first name and surname for direct matches.
Influence ordering a bit to always prefer direct matches before phonetic variations.
Prerequisites:
firstname index field that stores <first name> (non-analyzed, lowercased)
surname index field that stores <surname> (non-analyzed, lowercased)
Refine the query
bq=firstname:("mustermann")surname:("mustermann")

Tuning BMPM using additional mechanisms yields well-grounded phonetic matches.
What have we done?
Test the effect of BMPM parameterizations on your dataset
Add one-off-corrections to mitigate spelling mistakes that phonetics won't catch
Allow for displacement of max. two terms within a phrase
Boost on first and surname separately to influence relevance sorting

Tuning BMPM using additional mechanisms yields well-grounded phonetic matches.
Achievements
Good trade-off between precision and recall
usually top match on search for unique names
Result sets are explainable
Relevance ordering feels natural
direct matches, phonetic variations, one-off corrections

Phonetic Matching with Apache Solr

Phonetic Matching with Apache Solr

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Phonetic Matching with Apache Solr