Difference between revisions of "Building dictionaries"
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+ | [[Fabriquer des dictionnaires|En français]] |
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+ | |||
{{TOCD}} |
{{TOCD}} |
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Some of you have been brave enough to start to write new language pairs |
Some of you have been brave enough to start to write new language pairs |
||
for Apertium. That makes me (and all of the Apertium troop) very happy |
for Apertium. That makes me (and all of the Apertium troop) very happy |
||
− | and thankful, but |
+ | and thankful, but more importantly, it makes Apertium useful to more |
people. |
people. |
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− | + | I want to share some lessons I have learned after building |
|
some dictionaries: the importance of frequency estimates. For the new |
some dictionaries: the importance of frequency estimates. For the new |
||
pairs to have the best possible coverage with a minimum of effort, it is |
pairs to have the best possible coverage with a minimum of effort, it is |
||
− | very important to add words and rules in decreasing frequency, starting |
+ | very important to '''add words and rules in decreasing frequency''', starting |
with the most frequent words and phenomena. |
with the most frequent words and phenomena. |
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+ | |||
+ | The reason that words should be added in order of frequency is quite intuitive: |
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+ | the higher the frequency, the more likely the word is to appear in the text you are |
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+ | trying to translate (see below for Zipf's law). |
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+ | |||
+ | For example, in English you can almost be sure that the words "the" or |
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+ | "a" will appear in all but the most basic sentences; however, how many |
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+ | times have you seen "hypothyroidism" or "obelisk" written? The higher the frequency of |
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+ | the word, the more you "gain" from adding it. |
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==Frequency== |
==Frequency== |
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− | A person's intuition on which words are important |
+ | A person's intuition on which words are important or frequent can be |
very deceptive. Therefore, the best one can do is collect a lot of text |
very deceptive. Therefore, the best one can do is collect a lot of text |
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− | (millions of words if possible) which is representative of what one |
+ | (millions of words, if possible) which is representative of what one |
wants to translate, and study the frequencies of words and phenomena. |
wants to translate, and study the frequencies of words and phenomena. |
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− | Get it from Wikipedia |
+ | Get it from Wikipedia or from a newspaper archive, or write a robot that harvests |
− | it from the |
+ | it from the Web. |
It is quite easy to make a crude "hit parade" of words using a simple |
It is quite easy to make a crude "hit parade" of words using a simple |
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− | Unix command sequence (a single line) |
+ | Unix command sequence (a single line): |
+ | |||
<pre> |
<pre> |
||
$ cat mybigrepresentative.txt | tr ' ' '\012' | sort -f | uniq -c | sort -nr > hitparade.txt |
$ cat mybigrepresentative.txt | tr ' ' '\012' | sort -f | uniq -c | sort -nr > hitparade.txt |
||
</pre> |
</pre> |
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− | [I took this from Unix for Poets I think] |
+ | [I took this from ''Unix for Poets'', I think. More info on making frequency lists at [[Make a frequency list]].) |
Of course, this may be improved a lot but serves for illustration |
Of course, this may be improved a lot but serves for illustration |
||
purposes. |
purposes. |
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− | [[Image:Wikipedia-n-zipf.png|thumb|320px|right|'''Word frequency vs. Word rank''': A plot of word frequency in Wikipedia. The plot is in log-log coordinates. |
+ | [[Image:Wikipedia-n-zipf.png|thumb|320px|right|'''Word frequency vs. Word rank''': A plot of word frequency in Wikipedia. The plot is in log-log coordinates. ''X'' is the rank of a word in the frequency table; ''Y'' is the total number of the word’s occurences. Zipf's law corresponds to the upper linear portion of the curve, roughly following the green (1/''x'') line.]] |
− | You will find interesting properties in this list. |
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− | One is that multiplying the rank of a word by its frequency, you get a |
+ | You will find interesting properties in this list. One is that in multiplying the rank of a word by its frequency, you get a number which is pretty constant. That's called [http://en.wikipedia.org/wiki/Zipf%27s_law Zipf's Law]. |
− | number which is pretty constant. That's called [http://en.wikipedia.org/wiki/Zipf%27s_law Zipf's Law]. |
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− | + | Another one is that '''half of the list''' are ''hapax legomena'' (words that appear only once). |
|
− | appear only once). |
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− | + | Third, with about 1,000 words you may have 75% of the text covered. |
|
So use lists like these when you are building dictionaries. |
So use lists like these when you are building dictionaries. |
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− | If one of your |
+ | If one of your languages is English, there are interesting lists: |
* [http://ogden.basic-english.org/words.html Ogden's Basic English] (850 words) |
* [http://ogden.basic-english.org/words.html Ogden's Basic English] (850 words) |
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* [http://www.voanews.com/specialenglish Voice of America's Special English] |
* [http://www.voanews.com/specialenglish Voice of America's Special English] |
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− | + | Bear in mind, of course, that these lists are also based on a particular usage model of English, which is not "naturally occurring" English. |
|
− | model of English, which is not "natural occurring" English. |
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The same applies for other linguistic phenomena. Linguists tend to focus |
The same applies for other linguistic phenomena. Linguists tend to focus |
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are usually not the "building blocks" you would use to build translation |
are usually not the "building blocks" you would use to build translation |
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rules. So do not get carried away. Trust only frequencies and lots of |
rules. So do not get carried away. Trust only frequencies and lots of |
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− | real text |
+ | real text. |
+ | ===Sorting part of a bidix by frequency=== |
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− | ==Wikipedia dumps== |
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+ | Say you've created your hitparade, here's a way of sorting part of a bidix (e.g. all nouns) by frequency. |
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+ | |||
+ | First, extract the part you want to sort into a file of its own, e.g. <code>/tmp/nouns</code> like |
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+ | <pre> |
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+ | <e> <p><l>potetlauv<s n="n"/><s n="nt"/></l><r>potetløv<s n="n"/><s n="nt"/></r></p></e> |
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+ | <e r="RL"><p><l>potetlauv<s n="n"/><s n="nt"/></l><r>potetlauv<s n="n"/><s n="nt"/></r></p></e> |
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+ | <e> <p><l>haustlauv<s n="n"/><s n="nt"/></l><r>høstløv<s n="n"/><s n="nt"/></r></p></e> |
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+ | <e> <p><l>klauvdyr<s n="n"/><s n="nt"/></l><r>klovdyr<s n="n"/><s n="nt"/></r></p></e> |
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+ | </pre> |
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+ | You'll probably want to keep all nouns in one place, so do this once per part-of-speech. Now, if you've also created a file /tmp/hitparade.txt as described above, run the following script in your terminal: |
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+ | |||
+ | <pre> |
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+ | </tmp/hitparade.txt sed 's/^ *\([0-9][0-9]*\) */\1\t/' | awk -F'\t' -vside="l" -ventries=/tmp/nouns ' |
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+ | BEGIN{ |
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+ | while(getline<entries){ |
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+ | lm=$0; sub(".*<"side">","",lm);sub(/<s .*/,"",lm);gsub("<[bj]/>"," ",lm);gsub("</*[ag]/*>","",lm) |
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+ | if(lm in lines) lines[lm]=lines[lm]"\n" |
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+ | lines[lm]=lines[lm]$0 |
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+ | } |
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+ | } |
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+ | $2 in lines { |
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+ | print lines[$2] |
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+ | lines[$2]=0 |
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+ | } |
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+ | END{for(lm in lines){ if(lines[lm]) print lines[lm] }}' |
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+ | ' >/tmp/nouns.freqsorted |
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+ | </pre> |
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+ | |||
+ | Remember to replace <code>/tmp/hitparade.txt</code> and <code>/tmp/nouns</code> with whatever file names you used, and replace <code>-vside="l"</code> with <code>-vside="r"</code> if you want to sort by <r> instead of <l>. |
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+ | |||
+ | Now you can replace the lines in bidix which you cut out, with the contents of <code>/tmp/nouns.freqsorted</code>. |
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+ | |||
+ | ==Getting corpora== |
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+ | |||
+ | ===Corpus catcher=== |
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+ | |||
+ | * http://translate.sourceforge.net/wiki/corpuscatcher/index |
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+ | |||
+ | ===Wikipedia dumps=== |
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* http://download.wikimedia.org/backup-index.html |
* http://download.wikimedia.org/backup-index.html |
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− | For help in processing them see: |
+ | For help in processing them, see: |
− | * http:// |
+ | * http://www.mediawiki.org/wiki/Help:Export |
The dumps need cleaning up (removing Wiki syntax and XML etc.), but can |
The dumps need cleaning up (removing Wiki syntax and XML etc.), but can |
||
− | provide a ''substantial'' amount of text |
+ | provide a ''substantial'' amount of text — both for frequency analysis and |
− | sentences for POS [[tagger training]]. It can take some work, and isn't as |
+ | as a source of sentences for POS [[tagger training]]. It can take some work, and isn't as |
easy as getting a nice corpus, but on the other hand they're available |
easy as getting a nice corpus, but on the other hand they're available |
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+ | in some [http://meta.wikimedia.org/wiki/List_of_Wikipedias 275 languages] with at least 100 articles written in each. |
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− | in ~270 languages. |
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You'll want the one entitled "Articles, templates, image descriptions, |
You'll want the one entitled "Articles, templates, image descriptions, |
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Line 75: | Line 122: | ||
content, and is the archive most mirror sites will probably want." |
content, and is the archive most mirror sites will probably want." |
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+ | Use one of the methods on [[Wikipedia dumps]] to clean your corpus. |
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− | Something like (for Afrikaans): |
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+ | |||
+ | Or a simple shell script like (for Afrikaans): |
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<pre> |
<pre> |
||
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed |
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed |
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's/$/\n/g' | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | |
's/$/\n/g' | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | |
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− | sed 's/& |
+ | sed 's/&[a-z]*;/ /g' |
</pre> |
</pre> |
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+ | or more simple : |
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− | Will give you approximately useful lists of one sentence per line |
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+ | <pre> |
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− | (stripping out most of the extraneous formatting). |
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+ | $ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | tr -d "[]" | |
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+ | sed 's/|/ /g |
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+ | s/&[a-z]*;/ /g' |
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+ | </pre> [[User:Bech|Bech]] 18:41, 6 February 2012 (UTC) |
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+ | |||
+ | This will give you approximately useful lists of one sentence per line |
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+ | (stripping out most of the extraneous formatting). Note, this presumes that your |
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+ | language uses the Latin alphabet; if it uses another writing system, |
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+ | you'll need to change that. |
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Try something like (for Afrikaans): |
Try something like (for Afrikaans): |
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<pre> |
<pre> |
||
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed 's/$/\n/g' | |
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed 's/$/\n/g' | |
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− | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | sed 's/& |
+ | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | sed 's/&[a-z]*;/ /g' | tr ' ' '\012' | |
sort -f | uniq -c | sort -nr > hitparade.txt |
sort -f | uniq -c | sort -nr > hitparade.txt |
||
</pre> |
</pre> |
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+ | |||
+ | or more simple : |
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+ | <pre> |
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+ | $ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | tr -d "[]" | |
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+ | sed 's/|/ /g |
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+ | s/&[a-z]*;/ /g |
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+ | s/ /\n/g' | sort -f | uniq -c | sort -nr > hitparade.txt |
||
+ | </pre> [[User:Bech|Bech]] 18:41, 6 February 2012 (UTC) |
||
Once you have this 'hitparade' of words, it is first probably best to skim |
Once you have this 'hitparade' of words, it is first probably best to skim |
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− | off the top 20& |
+ | off the top 20,000–30,000 into a separate file. |
<pre> |
<pre> |
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Line 101: | Line 167: | ||
</pre> |
</pre> |
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− | Now, if you already have been working on a dictionary |
+ | Now, if you already have been working on a dictionary, chances are that there |
will exist in this 'top list' words you have already added. You can remove word forms |
will exist in this 'top list' words you have already added. You can remove word forms |
||
you are already able to analyse using (for example Afrikaans): |
you are already able to analyse using (for example Afrikaans): |
||
<pre> |
<pre> |
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− | $ cat top.lista.20000.txt | lt-proc |
+ | $ cat top.lista.20000.txt | apertium-destxt | lt-proc af-en.automorf.bin | apertium-retxt | grep '\/\*' > words_to_be_added.txt |
</pre> |
</pre> |
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+ | (here <code>lt-proc af-en.automorf.bin</code> will analyse the input stream of Afrikaans words and put an asterisk * on those it doesn't recognise) |
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− | Every 10 words or so you add, its probably worth going back and repeating this step, especially |
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+ | |||
− | for highly inflected languages — as one lemma can produce many word forms and the wordlist |
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+ | For every 10 words or so you add, it's probably worth going back and repeating this step, especially |
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+ | for highly inflected languages — as one lemma can produce many word forms, and the wordlist |
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is not lemmatised. |
is not lemmatised. |
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− | == |
+ | ==Generating monolingual dictionary entries== |
+ | {{main|Monodix}} |
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+ | |||
+ | Cheap tricks and clever hacks to generate monodix entries: |
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+ | * Run [[Extract]] |
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+ | ** Maybe use your existing monodix to constrain the possible pardefs, see [[Improved corpus-based paradigm matching]] |
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+ | * See more ideas at [[Speeding up monodix creation]] |
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+ | * Or follow the [[#Simple corpus-based paradigm-matching|below procedure]] |
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+ | |||
+ | ===Simple corpus-based paradigm-matching=== |
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+ | If the language you're working with is fairly regular, and noun inflection is quite easy (for example English or Afrikaans), then the following script may be useful: |
||
+ | |||
+ | You'll need a large wordlist (of all forms, not just lemmata) and some existing paradigms. It works by first taking all singular forms out of the list, then looking for plural forms, then printing out those which have both singular and plural forms in Apertium format. |
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+ | |||
+ | ''Note: These will need to be checked, as no language except Esperanto is that regular.'' |
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+ | |||
+ | <pre> |
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+ | # set this to the location of your wordlist |
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+ | WORDLIST=/home/spectre/corpora/afrikaans-meester-utf8.txt |
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+ | |||
+ | # set the paradigm, and the singular and plural endings. |
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+ | PARADIGM=sa/ak__n |
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+ | SINGULAR=aak |
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+ | PLURAL=ake |
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+ | # set this to the number of characters that need to be kept from the singular form. |
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+ | # e.g. [0:-1] means 'cut off one character', [0:-2] means 'cut off two characters' etc. |
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+ | ECHAR=`echo -n $SINGULAR | python -c 'import sys; print sys.stdin.read().decode("utf8")[0:-1];' |
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+ | |||
+ | PLURALS=`cat $WORDLIST | grep $PLURAL$` |
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+ | SINGULARS=`cat $WORDLIST | grep $SINGULAR$` |
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+ | CROSSOVER="" |
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+ | |||
+ | for word in $PLURALS; do |
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+ | SFORM=`echo $word | sed "s/$PLURAL/$SINGULAR/g"` |
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+ | cat $WORDLIST | grep ^$SFORM$ > /dev/null |
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+ | # if the form is found then append it to the list |
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+ | if [ $? -eq 0 ]; then |
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+ | CROSSOVER=$CROSSOVER" "$SFORM |
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+ | fi |
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+ | done |
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+ | |||
+ | # print out the list |
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+ | for pair in $CROSSOVER; do |
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+ | echo ' <e lm="'$pair'"><i>'`echo $pair | sed "s/$SINGULAR/$ECHAR/g"`'</i><par n="'$PARADIGM'"/></e>'; |
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+ | done |
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+ | </pre> |
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+ | |||
+ | ==Generating bilingual dictionary entries== |
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+ | Cheap tricks and clever hacks for expanding bilingual dictionaries: |
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+ | |||
+ | * [[Crossdics|Cross two language pairs with crossdics]] – turn A-B and B-C into A-C |
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+ | |||
+ | * [[Getting bilingual dictionaries from OmegaWiki|Get cheap bilingual dictionaries from OmegaWiki]] |
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+ | |||
+ | * [[Bilingual dictionary discovery]] for more complicated Crossdics-like pairings |
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+ | |||
+ | * Make a TMX with [[bitextor]] |
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+ | |||
+ | * Grab some translations from [[Wikidata]] |
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+ | |||
+ | * Run [[ReTraTos]] on word alignments if you have a parallel corpus |
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+ | ** or just [[Giza]] and look through the symmetrised dictionary created; see [[Extracting bilingual dictionaries with Giza++]] |
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+ | ** or just [[Anymalign]] / [[Aligning_a_corpus_with_fast_align|fast_align]] and look through the generated word alignments |
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+ | |||
+ | * Spell a source language frequency list using a target-language spell checker |
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+ | ** maybe run a transliterator first |
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+ | ** maybe filter candidates by occurrence in an aligned sentence |
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+ | ** maybe run some regexes on it first to hardcode some subword correspondences |
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+ | *** or even create a full-blown sub-word translator using the regex/FST system of your choice |
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+ | |||
+ | * Decompound words and run through your existing bidix |
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+ | ** See [[Automatically generating compound bidix entries]] for one approach, scoring with an LM |
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+ | ** Maybe decompound your ''bidix'' as well (requires an analyser that can give compound analyses even if full analyses exist) and use that to create a bigger compound-part-bidix to run the above procedure on |
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+ | |||
+ | * Cluster compound-parts to find translations of parts through compounds with other parts |
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+ | ** Say you have big monolingual dictionaries and want to expand the bidix. E.g. the sme word bearaš is missing in sme-nob, but it appears as a prefix in lots of monolingual compounds, e.g. bearašviessu, bearašásodat. Many of the _suffixes_ are in the bidix. So look up these suffixes, and find which nob-words take the translations of the suffixes. The ones that take the most of these suffix-translations are likely candidates for a prefix-translation. |
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+ | |||
+ | * Generate possible Multiword Compounds from existing multiword compounds, check for them in corpus |
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+ | ** as done in http://www.mt-archive.info/10/MTS-2013-Sato.pdf |
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+ | ** if your bidix has mwe's "A X", "B X" and "A Y", then a candidate mwe is "A X" |
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+ | |||
+ | * Find Repeated Words within two aligned paragraphs – these are possible translation terms |
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+ | ** as done in http://www.mt-archive.info/RANLP-2005-Giguet.pdf |
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+ | ** lemmatise first |
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+ | |||
+ | * Find Inline Bitext, terms translated into the target language occurring in a source-language text, by using word-level language identification |
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+ | ** as done in http://www-personal.umich.edu/~tdszyman/thesis.pdf |
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+ | |||
+ | * Use Wiktionary (see below) |
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+ | |||
+ | * Run [https://research.swtch.com/church-tetragram.pdf word_align] to align words using the tetragrams of a small bilingual corpus ("word_align and char_align were designed to work robustly on texts that are smaller and more noisy than the Hansards") |
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+ | |||
+ | |||
+ | Ways to filter out good candidates, in general: |
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+ | * Corpus frequency – always start at the top |
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+ | * Occurrence in aligned sentences – these are typically good |
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+ | ** maybe lemmatise corpus first, or look for word that's edit-distance<=2 from source-side of candidate word |
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+ | * Frequency similarity – source and target words should have "similar" normalised frequencies |
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+ | * Generated word has a monolingual analysis – assuming your monodix is bigger than your bidix |
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+ | |||
+ | |||
+ | ===Getting bilingual dictionary entries from Wiktionary=== |
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A cheap way of getting bilingual dictionary entries between a pair of |
A cheap way of getting bilingual dictionary entries between a pair of |
||
languages is as follows: |
languages is as follows: |
||
− | First grab yourself a wordlist of ''nouns'' in language ''x'' |
+ | First grab yourself a wordlist of ''nouns'' in language ''x''; for |
example, grab them out of the Apertium dictionary you are using: |
example, grab them out of the Apertium dictionary you are using: |
||
Line 150: | Line 319: | ||
</pre> |
</pre> |
||
− | ''Note: The "sleep 8" is so that we don't put undue strain on the Wikimedia servers'' |
+ | ''Note: The "sleep 8" is so that we don't put undue strain on the Wikimedia servers.'' |
− | + | If you save this as <code>iw-word.sh</code>, then you can use it at the command line: |
|
<pre> |
<pre> |
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$ sh iw-word.sh <wordlist> <language code from> <language code to> |
$ sh iw-word.sh <wordlist> <language code from> <language code to> |
||
</pre> |
</pre> |
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+ | For example, to retrieve a bilingual wordlist from English to Afrikaans, use: |
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− | |||
− | e.g. to retrieve a bilingual wordlist from English to Afrikaans, use: |
||
<pre> |
<pre> |
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Line 165: | Line 333: | ||
The method is of variable reliability. Reports of between 70% and 80% |
The method is of variable reliability. Reports of between 70% and 80% |
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− | accuracy are common. It is best for unambiguous terms, but works |
+ | accuracy are common. It is best for unambiguous terms, but works all right where |
terms retain ambiguity through languages. |
terms retain ambiguity through languages. |
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Line 171: | Line 339: | ||
fluent speakers of the language pairs in question. |
fluent speakers of the language pairs in question. |
||
− | == |
+ | ==Further reading== |
− | {{main|Monodix}} |
||
− | |||
− | If the language you're working with is fairly regular, and noun inflection is quite easy (for example English or Afrikaans) then the following script may be useful: |
||
− | |||
− | You'll need a large wordlist (of all forms, not just lemmata) and some existing paradigms. It works by first taking all singular forms out of the list, then looking for plural forms, then printing out those which have both singular and plural forms in Apertium format. |
||
− | |||
− | ''Note: These will need to be checked, as no language is that regular.'' |
||
− | |||
− | <pre> |
||
− | # set this to the location of your wordlist |
||
− | WORDLIST=/home/spectre/corpora/afrikaans-meester-utf8.txt |
||
− | |||
− | # set the paradigm, and the singular and plural endings. |
||
− | PARADIGM=ta/al__n |
||
− | SINGULAR=aak |
||
− | PLURAL=ake |
||
− | ECHAR=`echo $SINGULAR | head -c 1`; |
||
− | |||
− | PLURALS=`cat $WORDLIST | grep $PLURAL$` |
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− | SINGULARS=`cat $WORDLIST | grep $SINGULAR$` |
||
− | CROSSOVER="" |
||
− | |||
− | for word in $PLURALS; do |
||
− | SFORM=`echo $word | sed "s/$PLURAL/$SINGULAR/g"` |
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− | cat $WORDLIST | grep ^$SFORM$ > /dev/null |
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− | # if the form is found then append it to the list |
||
− | if [ $? -eq 0 ]; then |
||
− | CROSSOVER=$CROSSOVER" "$SFORM |
||
− | fi |
||
− | done |
||
− | |||
− | # print out the list |
||
− | for pair in $CROSSOVER; do |
||
− | echo ' <e lm="'$pair'"><i>'`echo $pair | sed "s/$SINGULAR/$ECHAR/g"`'</i><par n="'$PARADIGM'"/></e>'; |
||
− | done |
||
− | </pre> |
||
− | |||
− | ==See also== |
||
+ | * Mark Pagel, Quentin D. Atkinson & Andrew Meade (2007) "Frequency of word-use predicts rates of lexical evolution throughout Indo-European history". ''Nature'' 449, 665 |
||
− | * [[Crossdics]] |
||
+ | :"Across all 200 meanings, frequently used words evolve at slower rates and infrequently used words evolve more rapidly. This relationship holds separately and identically across parts of speech for each of the four language corpora, and accounts for approximately 50% of the variation in historical rates of lexical replacement. We propose that the frequency with which specific words are used in everyday language exerts a general and law-like influence on their rates of evolution." |
||
[[Category:Documentation]] |
[[Category:Documentation]] |
||
+ | [[Category:Writing dictionaries]] |
||
+ | [[Category:Documentation in English]] |
Latest revision as of 21:36, 15 March 2020
Some of you have been brave enough to start to write new language pairs for Apertium. That makes me (and all of the Apertium troop) very happy and thankful, but more importantly, it makes Apertium useful to more people.
I want to share some lessons I have learned after building some dictionaries: the importance of frequency estimates. For the new pairs to have the best possible coverage with a minimum of effort, it is very important to add words and rules in decreasing frequency, starting with the most frequent words and phenomena.
The reason that words should be added in order of frequency is quite intuitive: the higher the frequency, the more likely the word is to appear in the text you are trying to translate (see below for Zipf's law).
For example, in English you can almost be sure that the words "the" or "a" will appear in all but the most basic sentences; however, how many times have you seen "hypothyroidism" or "obelisk" written? The higher the frequency of the word, the more you "gain" from adding it.
Frequency[edit]
A person's intuition on which words are important or frequent can be very deceptive. Therefore, the best one can do is collect a lot of text (millions of words, if possible) which is representative of what one wants to translate, and study the frequencies of words and phenomena. Get it from Wikipedia or from a newspaper archive, or write a robot that harvests it from the Web.
It is quite easy to make a crude "hit parade" of words using a simple Unix command sequence (a single line):
$ cat mybigrepresentative.txt | tr ' ' '\012' | sort -f | uniq -c | sort -nr > hitparade.txt
[I took this from Unix for Poets, I think. More info on making frequency lists at Make a frequency list.)
Of course, this may be improved a lot but serves for illustration purposes.
![](/w/images/thumb/b/b9/Wikipedia-n-zipf.png/320px-Wikipedia-n-zipf.png)
You will find interesting properties in this list. One is that in multiplying the rank of a word by its frequency, you get a number which is pretty constant. That's called Zipf's Law.
Another one is that half of the list are hapax legomena (words that appear only once).
Third, with about 1,000 words you may have 75% of the text covered.
So use lists like these when you are building dictionaries.
If one of your languages is English, there are interesting lists:
Bear in mind, of course, that these lists are also based on a particular usage model of English, which is not "naturally occurring" English.
The same applies for other linguistic phenomena. Linguists tend to focus on very infrequent phenomena which are key to the identity of a language, or on what is different between languages. But these "jewels" are usually not the "building blocks" you would use to build translation rules. So do not get carried away. Trust only frequencies and lots of real text.
Sorting part of a bidix by frequency[edit]
Say you've created your hitparade, here's a way of sorting part of a bidix (e.g. all nouns) by frequency.
First, extract the part you want to sort into a file of its own, e.g. /tmp/nouns
like
<e> <p><l>potetlauv<s n="n"/><s n="nt"/></l><r>potetløv<s n="n"/><s n="nt"/></r></p></e> <e r="RL"><p><l>potetlauv<s n="n"/><s n="nt"/></l><r>potetlauv<s n="n"/><s n="nt"/></r></p></e> <e> <p><l>haustlauv<s n="n"/><s n="nt"/></l><r>høstløv<s n="n"/><s n="nt"/></r></p></e> <e> <p><l>klauvdyr<s n="n"/><s n="nt"/></l><r>klovdyr<s n="n"/><s n="nt"/></r></p></e>
You'll probably want to keep all nouns in one place, so do this once per part-of-speech. Now, if you've also created a file /tmp/hitparade.txt as described above, run the following script in your terminal:
</tmp/hitparade.txt sed 's/^ *\([0-9][0-9]*\) */\1\t/' | awk -F'\t' -vside="l" -ventries=/tmp/nouns ' BEGIN{ while(getline<entries){ lm=$0; sub(".*<"side">","",lm);sub(/<s .*/,"",lm);gsub("<[bj]/>"," ",lm);gsub("</*[ag]/*>","",lm) if(lm in lines) lines[lm]=lines[lm]"\n" lines[lm]=lines[lm]$0 } } $2 in lines { print lines[$2] lines[$2]=0 } END{for(lm in lines){ if(lines[lm]) print lines[lm] }}' ' >/tmp/nouns.freqsorted
Remember to replace /tmp/hitparade.txt
and /tmp/nouns
with whatever file names you used, and replace -vside="l"
with -vside="r"
if you want to sort by <r> instead of <l>.
Now you can replace the lines in bidix which you cut out, with the contents of /tmp/nouns.freqsorted
.
Getting corpora[edit]
Corpus catcher[edit]
Wikipedia dumps[edit]
For help in processing them, see:
The dumps need cleaning up (removing Wiki syntax and XML etc.), but can provide a substantial amount of text — both for frequency analysis and as a source of sentences for POS tagger training. It can take some work, and isn't as easy as getting a nice corpus, but on the other hand they're available in some 275 languages with at least 100 articles written in each.
You'll want the one entitled "Articles, templates, image descriptions, and primary meta-pages. -- This contains current versions of article content, and is the archive most mirror sites will probably want."
Use one of the methods on Wikipedia dumps to clean your corpus.
Or a simple shell script like (for Afrikaans):
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed 's/$/\n/g' | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | sed 's/&[a-z]*;/ /g'
or more simple :
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | tr -d "[]" | sed 's/|/ /g s/&[a-z]*;/ /g'
Bech 18:41, 6 February 2012 (UTC)
This will give you approximately useful lists of one sentence per line (stripping out most of the extraneous formatting). Note, this presumes that your language uses the Latin alphabet; if it uses another writing system, you'll need to change that.
Try something like (for Afrikaans):
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed 's/$/\n/g' | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | sed 's/&[a-z]*;/ /g' | tr ' ' '\012' | sort -f | uniq -c | sort -nr > hitparade.txt
or more simple :
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | tr -d "[]" | sed 's/|/ /g s/&[a-z]*;/ /g s/ /\n/g' | sort -f | uniq -c | sort -nr > hitparade.txt
Bech 18:41, 6 February 2012 (UTC)
Once you have this 'hitparade' of words, it is first probably best to skim off the top 20,000–30,000 into a separate file.
$ cat hitparade.txt | head -20000 > top.lista.20000.txt
Now, if you already have been working on a dictionary, chances are that there will exist in this 'top list' words you have already added. You can remove word forms you are already able to analyse using (for example Afrikaans):
$ cat top.lista.20000.txt | apertium-destxt | lt-proc af-en.automorf.bin | apertium-retxt | grep '\/\*' > words_to_be_added.txt
(here lt-proc af-en.automorf.bin
will analyse the input stream of Afrikaans words and put an asterisk * on those it doesn't recognise)
For every 10 words or so you add, it's probably worth going back and repeating this step, especially for highly inflected languages — as one lemma can produce many word forms, and the wordlist is not lemmatised.
Generating monolingual dictionary entries[edit]
- Main article: Monodix
Cheap tricks and clever hacks to generate monodix entries:
- Run Extract
- Maybe use your existing monodix to constrain the possible pardefs, see Improved corpus-based paradigm matching
- See more ideas at Speeding up monodix creation
- Or follow the below procedure
Simple corpus-based paradigm-matching[edit]
If the language you're working with is fairly regular, and noun inflection is quite easy (for example English or Afrikaans), then the following script may be useful:
You'll need a large wordlist (of all forms, not just lemmata) and some existing paradigms. It works by first taking all singular forms out of the list, then looking for plural forms, then printing out those which have both singular and plural forms in Apertium format.
Note: These will need to be checked, as no language except Esperanto is that regular.
# set this to the location of your wordlist WORDLIST=/home/spectre/corpora/afrikaans-meester-utf8.txt # set the paradigm, and the singular and plural endings. PARADIGM=sa/ak__n SINGULAR=aak PLURAL=ake # set this to the number of characters that need to be kept from the singular form. # e.g. [0:-1] means 'cut off one character', [0:-2] means 'cut off two characters' etc. ECHAR=`echo -n $SINGULAR | python -c 'import sys; print sys.stdin.read().decode("utf8")[0:-1];' PLURALS=`cat $WORDLIST | grep $PLURAL$` SINGULARS=`cat $WORDLIST | grep $SINGULAR$` CROSSOVER="" for word in $PLURALS; do SFORM=`echo $word | sed "s/$PLURAL/$SINGULAR/g"` cat $WORDLIST | grep ^$SFORM$ > /dev/null # if the form is found then append it to the list if [ $? -eq 0 ]; then CROSSOVER=$CROSSOVER" "$SFORM fi done # print out the list for pair in $CROSSOVER; do echo ' <e lm="'$pair'"><i>'`echo $pair | sed "s/$SINGULAR/$ECHAR/g"`'</i><par n="'$PARADIGM'"/></e>'; done
Generating bilingual dictionary entries[edit]
Cheap tricks and clever hacks for expanding bilingual dictionaries:
- Cross two language pairs with crossdics – turn A-B and B-C into A-C
- Bilingual dictionary discovery for more complicated Crossdics-like pairings
- Make a TMX with bitextor
- Grab some translations from Wikidata
- Run ReTraTos on word alignments if you have a parallel corpus
- or just Giza and look through the symmetrised dictionary created; see Extracting bilingual dictionaries with Giza++
- or just Anymalign / fast_align and look through the generated word alignments
- Spell a source language frequency list using a target-language spell checker
- maybe run a transliterator first
- maybe filter candidates by occurrence in an aligned sentence
- maybe run some regexes on it first to hardcode some subword correspondences
- or even create a full-blown sub-word translator using the regex/FST system of your choice
- Decompound words and run through your existing bidix
- See Automatically generating compound bidix entries for one approach, scoring with an LM
- Maybe decompound your bidix as well (requires an analyser that can give compound analyses even if full analyses exist) and use that to create a bigger compound-part-bidix to run the above procedure on
- Cluster compound-parts to find translations of parts through compounds with other parts
- Say you have big monolingual dictionaries and want to expand the bidix. E.g. the sme word bearaš is missing in sme-nob, but it appears as a prefix in lots of monolingual compounds, e.g. bearašviessu, bearašásodat. Many of the _suffixes_ are in the bidix. So look up these suffixes, and find which nob-words take the translations of the suffixes. The ones that take the most of these suffix-translations are likely candidates for a prefix-translation.
- Generate possible Multiword Compounds from existing multiword compounds, check for them in corpus
- as done in http://www.mt-archive.info/10/MTS-2013-Sato.pdf
- if your bidix has mwe's "A X", "B X" and "A Y", then a candidate mwe is "A X"
- Find Repeated Words within two aligned paragraphs – these are possible translation terms
- as done in http://www.mt-archive.info/RANLP-2005-Giguet.pdf
- lemmatise first
- Find Inline Bitext, terms translated into the target language occurring in a source-language text, by using word-level language identification
- Use Wiktionary (see below)
- Run word_align to align words using the tetragrams of a small bilingual corpus ("word_align and char_align were designed to work robustly on texts that are smaller and more noisy than the Hansards")
Ways to filter out good candidates, in general:
- Corpus frequency – always start at the top
- Occurrence in aligned sentences – these are typically good
- maybe lemmatise corpus first, or look for word that's edit-distance<=2 from source-side of candidate word
- Frequency similarity – source and target words should have "similar" normalised frequencies
- Generated word has a monolingual analysis – assuming your monodix is bigger than your bidix
Getting bilingual dictionary entries from Wiktionary[edit]
A cheap way of getting bilingual dictionary entries between a pair of languages is as follows:
First grab yourself a wordlist of nouns in language x; for example, grab them out of the Apertium dictionary you are using:
$ cat <monolingual dictionary> | grep '<i>' | grep '__n\"' | awk -F'"' '{print $2}'
Next, write a basic script, something like:
#!/bin/sh #language to translate from LANGF=$2 #language to translate to LANGT=$3 #filename of wordlist LIST=$1 for LWORD in `cat $LIST`; do TEXT=`wget -q http://$LANGF.wikipedia.org/wiki/$LWORD -O - | grep 'interwiki-'$LANGT`; if [ $? -eq '0' ]; then RWORD=`echo $TEXT | cut -f4 -d'"' | cut -f5 -d'/' | python -c 'import urllib, sys; print urllib.unquote(sys.stdin.read());' | sed 's/(\w*)//g'`; echo '<e><p><l>'$LWORD'<s n="n"/></l><r>'$RWORD'<s n="n"/></r></p></e>'; fi; sleep 8; done
Note: The "sleep 8" is so that we don't put undue strain on the Wikimedia servers.
If you save this as iw-word.sh
, then you can use it at the command line:
$ sh iw-word.sh <wordlist> <language code from> <language code to>
For example, to retrieve a bilingual wordlist from English to Afrikaans, use:
$ sh iw-word.sh en-af.wordlist en af
The method is of variable reliability. Reports of between 70% and 80% accuracy are common. It is best for unambiguous terms, but works all right where terms retain ambiguity through languages.
Any correspondences produced by this method must be checked by native or fluent speakers of the language pairs in question.
Further reading[edit]
- Mark Pagel, Quentin D. Atkinson & Andrew Meade (2007) "Frequency of word-use predicts rates of lexical evolution throughout Indo-European history". Nature 449, 665
- "Across all 200 meanings, frequently used words evolve at slower rates and infrequently used words evolve more rapidly. This relationship holds separately and identically across parts of speech for each of the four language corpora, and accounts for approximately 50% of the variation in historical rates of lexical replacement. We propose that the frequency with which specific words are used in everyday language exerts a general and law-like influence on their rates of evolution."