Node: Phonetic Code, Next: The Simple Soundslike, Previous: Compiling the Word List, Up: Adding Support For Other Languages
Aspell is in fact the spell checker that comes up with the best suggestions if it finds an unknown word. One reason is that it does not just compare the word with other words in the dictionary (like Ispell does) but also uses phonetic comparisons with other words.
The new table driven phonetic code is very flexible and setting up phonetic transformation rules for other languages is not difficult but there can be a number of stumbling blocks -- that's why I wrote this section.
The main phonetic code is free of any language specific code and should be powerful enough to allow setting up rules for any language. Anything which is language specific is kept in a plain text file and can easily be edited. So it's even possible to write phonetic transformation rules if you don't have any programming skills. All you need to know is how words of the language are written and how they are pronounced.
In the translation array there are two strings on each line; the first one is the search string (or switch name) and the second one is the replacement string (or switch parameter). The line
version version
is also required to appear somewhere in the translation array. The
version string can be anything but it should be changed whenever a new
version of the translation array is released. This is important
because it will keep Aspell from using a compiled dictionary with the
wrong set of rules. For example, if when coming up with suggestion
for hallo
, Aspell will use the new rules to come up with the
soundslike say H*L*
, but if hello
is stored in the
dictionary using the old rules as HL
instead of H*L*
Aspell will never be able to come up with hello
. So to solve
this problem Aspell checks if the version strings match and aborts
with an error if they don't. Thus it is important to update it
whenever a new version of the translation array is released. This is
only a problem with the main word list as the personal word lists are
now stored as simple word lists with a single header line (i.e. no
soundslike data).
Each non switch line represents one replacement (transformation) rule. Words beginning with the same letter must be grouped together; the order inside this group does not depend on alphabetical issues but it gives priorities; the higher the rule the higher the priority. That's why the first rule that matches is applied. In the following example:
GH _ G K
GH -> _
has higher priority than G -> K
_
represents the empty string "". If GH -> _
came
after G -> K
, the second rule would never match because the
algorithm would stop searching for more rules after the first match.
The above rules transform any GH
to an empty string (delete
them) and transforms any other G
to K
.
At the end of the first string of a line (the search string) there may
optionally stand a number of characters in brackets. One (only one!)
of these characters must fit. It's comparable with the [ ]
brackets in regular expressions. The rule DG(EIY) -> J
for
example would match any DGE
, DGI
and
DGY
and replace them with J
. This way you can
reduce several rules to one.
Before the search string, one or more dashes -
may be placed.
Those search strings will be matched totally but only the beginning of
the string will be replaced. Furthermore, for these rules no follow-up
rule will be searched (what this is will be explained later). The
rule TCH--
-> _ will match any word containing
TCH
(like match
) but will only replace the first
character T
with an empty string. The number of dashes
determines how many characters from the end will not be replaced.
After the replacement, the search for transformation rules continues
with the not replaced CH
!
If a <
is appended to the search string, the search for
replacement rules will continue with the replacement string and not with
the next character of the word. The rule PH< -> F
for example
would replace PH
with F
and then again start to search for
a replacement rule for F...
. If there would also be rules
like FO
-> O
and F -> _
then words like
PHOXYZ
would be transformed to OXYZ
and any occurrences of
PH
that are not followed by an O
will be deleted like
PHIXYZ -> IXYZ
. The second replacement however is not applied if
the priority of this rule is lower than the priority of the first rule.
Priorities are added to a rule by putting a number between 0 and 9 at
the end of the search string, for example ING6 -> N
.
The higher the number the higher is the priority.
Priorities are especially important for the previously mentioned follow-up rules. Follow-up rules are searched beginning from the last string of the first search string. This is a bit complicated but I hope this example will make it clearer:
CHS X CH G HAU--1 H SCH SH
In this example CHS
in the word FUCHS
would be
transformed to X
. If we take the word DURCHSCHNITT
then
things look a bit different. Here CH
belongs together and
SCH
belongs together and both are spoken separately. The
algorithm however first finds the string CHS
which may not be
transformed like in the previous word FUCHS
. At this point the
algorithm can find a follow up rule. It takes the last character of
the first matching rule (CHS
) which is S
and looks for
the next match, beginning from this character. What it finds is
clear: It finds SCH -> SH
, which has the same priority
(no priority means standard priority, which is 5). If the priority is
the same or higher the follow-up rule will be applied. Let's take a
look at the word SCHAUKEL
. In this word SCH
belongs
together and may not be taken apart. After the algorithm has found
SCH
-> SH
it searches for a follow-up rule for
H+
AUKEL
. It finds HAU--1 -> H
, but does not
apply it because its priority is lower than the one of the first rule.
You see that this is a very powerful feature but it also can easily
lead to mistakes. If you really don't need this feature you can turn
it off by putting the line:
followup 0
at the beginning of the phonetic table file. As mentioned, for rules
containing a -
no follow-up rules are searched but giving such
rules a priority is not totally senseless because they can be
follow-up rules and in that case the priority makes sense again.
Follow-up rules of follow-up rules are not searched because this is in
fact not needed very often.
The control character ^
says that the search string only
matches at the beginning of words so that the rule RH -> R
will
only apply to words like RHESUS
but not PERHAPS
. You
can append another ^
to the search string. In that case the
algorithm treats the rest of the word totally separately from the
first matched string at the beginning. This is useful for prefixes
whose pronunciation does not depend on the rest of the word and vice
versa like OVER^^
in English for example.
The same way as ^
works does $
only apply to words
that end with the search string. GN$ -> N
only
matches on words like SIGN
but not SIGNUM
. If
you use ^
and $
together, both of them must fit
ENOUGH^$ -> NF
will only match the word
ENOUGH
and nothing else.
Of course you can combine all of the mentioned control characters but
they must occur in this order: < - priority ^ $
. All
characters must be written in CAPITAL letters.
If absolutely no rule can be found -- might happen if you use strange characters for which you don't have any replacement rule -- the next character will simply be skipped and the search for replacement rules will continue with the rest of the word.
If you want double letters to be reduced to one you must set up a rule
like LL- -> L
. If double letters in the resulting phonetic
word should be allowed, you must place the line:
collapse_result 0
at the beginning of your transformation table file; otherwise set the
value to `1'. The English rules for example strip all vowels from
words and so the word "GOGO" would be transformed to "K" and not to
"KK" (as desired) if collapse_result
is set to 1. That's why
the English rules have collapse_result
set to 0
.
By default, all accents are removed from a word before it is matched to the soundslike rules. If you do not want this then add the line
remove_accents 0
at the beginning of your file. The exact definition of an accent is language dependent and is controlled via the character set file. If you set remove_accents to '0' then you should also set "store-as" to "lower" in the language data file (not the phonetic transformation file) otherwise Aspell will have problems when both the accented and the de-accented version of a word appearing in the dictionary; it will consider one of them as incorrectly spelled.
Before you start to write an array of transformation rules, you should be aware that you have to do some work to make sure that things you do will result in correct transformation rules.
First of all, you need to have a large word list of the language you
want to make phonetics for. It should contain about as many words as
the dictionary of the spell checker. If you don't have such a list,
you will probably find an Ispell dictionary at
http://fmg-www.cs.ucla.edu/geoff/ispell-dictionaries.html which
will help you. You can then make affix expansion via ispell
-e
and then pipe it through tr " " "\n"
to put one word on
each line. After that you eventually have to convert special
characters like é
from Ispell's internal representation to
latin1 encoding. sed s/e'/é/g
for example would replace
all e'
with é
.
The second is that you know how to use regular expressions and know
how to use grep
. You should for example know that:
grep ^[^aeiou]qu[io] wordlist | less
will show you all words that begin with any character but a
,
e
, i
, o
or u
and then continue with
qui
or quo
. This stuff is important for example to
find out if a phonetic replacement rule you want to set up is valid
for all words which match the expression you want to replace. Taking
a look at the regex(7) man page is a good idea.
Normal text comparison works well as long as the typer misspells a word because he pressed one key he didn't really want to press. In these cases, mostly one character differs from the original word.
In cases where the writer didn't know about the correct spelling of the word, the word may have several characters that differ from the original word but usually the word would still sound like the original. Someone might think that `tough' is spelled `taff'. No spell checker without phonetic code will come to the idea that this might be `tough', but a spell checker who knows that `taff' would be pronounced like `tough' will make good suggestions to the user. Another example could be `funetik' and `phonetic'.
From these examples you can see that the phonetic transformation should not be too fussy and too precise. If you implement a whole phonetic dictionary as you can find it in books this will not be very useful because then there could still be many characters differing from the misspelled and the desired word. What you should do if you implement the phonetic transformation table is to reduce the number of used letters to the only really necessary ones.
Characters that sound similar should be reduced to one. In English
language for example `Z' sounds like `S' and that's why the
transformation rule Z -> S
is present in the
replacement table. "PH is spoken like "F and so we have a
PH -> F
rule.
If you take a closer look you will even see that vowels sound very similar in the English language: `contradiction', `cuntradiction', `cantradiction' or `centradiction' in fact sound nearly the same, don't they? Therefore the English phonetic replacement rules not only reduce all vowels to one but even remove them all (removing is done by just setting up no rule for those letters). The phonetic code of "contradiction" is "KNTRTKXN" and if you try to read this letter-monster loud you will hear that it still sound a bit like `contradiction'. You also see that "D" is transformed to "T" because they nearly sound the same.
If you think you have found a regularity you should always take
your word list and grep
for the corresponding regular
expression you want to make a transformation rule for. An example: If
you come to the idea that all English words ending on `ough' sound
like `AF' at the end because you think of `enough' and `tough'. If
you then grep
for the corresponding regular expression by
grep -i ough$ wordlist
you will see that the rule you wanted
to set up is not correct because the rule doesn't fit to words like
`although' or `bough'. So you have to define your rule more precisely
or you have to set up exceptions if the number of words that differ
from the desired rule is not too big.
Don't forget about follow-up rules which can help in many cases but
which also can lead to confusion and unwanted side effects. It's also
important to write exceptions in front of the more general rules
(GH
before G
etc.).
If you think you have set up a number of rules that may produce some
good results try them out! If you run Aspell as aspell
--lang=
your_language pipe
you get a prompt at which you can type
in words. If you just type words Aspell checks them and eventually
makes suggestions if they are misspelled. If you type in $$Sw
word you will see the phonetic transformation and you can test
out if your work does what you want.
Another good way to check that changes you make to your rules don't
have any bad side effects is to create another list from your word
list which contains not only the word of the word list but also the
corresponding phonetic version of this word on the same line. If you
do this once before the change and once after the change you can make
a diff (see man diff
) to see what really changed. To
do this use the command aspell --lang=
your_language
soundslike
. In this mode aspell will output the the original word
and then its soundslike separated by a tab character for each word you
give it. If you are interested in seeing how the algorithm works you
can download a set of useful programs from
http://members.xoom.com/maccy/spell/phonet-utils.tar.gz. This
includes a program that produces a list as mentioned above and another
program which illustrates how the algorithm works. It uses the same
transformation table as Aspell and so it helps a lot during the
process of creating a phonetic transformation table for Aspell.
During your work you should write down your basic ideas so that other people are able to understand what you did (and you still know about it after a few weeks). The English table has a huge documentation appended as an example.
Now you can start experimenting with all the things you just read and perhaps set up a nice phonetic transformation table for your language to help Aspell to come up with the best correction suggestions ever seen also for your language. Take a look at the Aspell homepage to see if there is already a transformation table for your language. If there is one you might also take a look at it to see if it could be improved.
If you think that this section helped you or if you think that this is just a waste of time you can send any feedback to bjoern.jacke@gmx.de.