I'm trying to come up with a way to estimate the number of English words a translation from Japanese will turn into. Japanese has three main scripts -- Kanji, Hiragana, and Katakana -- and each has a different average character-to-word ratio (Kanji being the lowest, Katakana the highest).
Examples:
computer: コンピュータ (Katakana - 6
characters); 計算機 (Kanji: 3
characters)
whale: くじら (Hiragana --
3 characters); 鯨 (Kanji: 1
character)
As data, I have a large glossary of Japanese words and their English translations, and a fairly large corpus of matched Japanese source documents and their English translations. I want to come up with a formula that will count numbers of Kanji, Hiragana, and Katakana characters in a source text, and estimate the number of English words this is likely to turn into.
Here's what Borland (now Embarcadero) thinks about English to non-English:
Length of English string (in characters)
Expected increase
1-5 100%
6-12 80%
13-20 60%
21-30 40%
31-50 20%
over 50 10%
I think you can sort of apply this (with some modification) for Japanese to non-Japanese.
Another element you might want to consider is the tone of the language. In English, instructions are phrased as an imperative as in "Press OK." But in Japanese language, imperatives are considered rude, and you must phrase instructions in honorific (or keigo) as in "OKボタンを押してください。"
Watch out for three-letter kanji combos. Many of the big words translate into three- or four- letter kanji combo such as 国際化(internationalization: 20 chars), 高可用性(high availability: 17 chars).
I would start with linear approximation: approx_english_words = a1*no_characters_in_script1 + a2 * no_chars_in_script2 + a3 * no_chars_in_script3, with the coefficients a1, a2, a3 fit from your data using linear least squares.
If this doesn't approximate very well, then look at the worst cases for the reasons they don't fit (specialized words, etc.).
In my experience as a translator and localization specialist, a good rule of thumb is 2 Japanese characters per English word.
As an experienced translator between Japanese and English, I can say that this is extremely difficult to quantify, but typically in my experience English text translated from Japanese is nearly 200% as many characters as the source text. In Japanese there are many culturally specific phrases and nouns that can't be translated literally and need to be explained in English.
When translating it is not unusual for me to take a single Japanese sentence and to make a single English paragraph out of it in order for the meaning to be communicated to the reader. Off the top of my here is an example:
「懐かしい」
This literally means nostalgic. However, in Japanese it can be used as a single phrase in an exclamation. Yet, in English in order to convey a feeling of nostalgia we require a lot more context. For instance, you may need to turn that single phrase into a sentence:
"As I walked by my old elementary school, I was flooded with memories of the past."
This is why machine translation between Japanese and English is impossible.
Well, it's a little more complex than just the number of characters in a noun compared to English, for instance, Japanese also has a different grammatical structure compared to English, so certain sentences would use MORE words in Japanese, and others would use LESS words. I don't really know Japanese, so please forgive me for using Korean as an example.
In Korean, a sentence is often shorter than an English sentence, due mainly to the fact that they are cut short by using context to fill in the missing words. For instance, saying "I love you" could be as short as 사랑해 ("sarang hae", simply the verb "love"), or as long as the fully qualified sentence 저는 당신을 살앙해요 (I [topic] you [object] love [verb + polite modifier]. In a text how it is written depends on context, which is usually set by earlier sentences in the paragraph.
Anyway, having an algorithm to actually KNOW this kind of thing would be very difficult, so you're probably much better off, just using statistics. What you should do is use random samples where the known Japanese texts, and English texts have the same meaning. The larger the sample (and the more random it is) the better... though if they are truly random, it won't make much difference how many you have past a few hundred.
Now, another thing is this ratio would change completely on the type of text being translated. For instance, highly technical document is quite likely to have a much higher Japanese/English length ratio than a soppy novel.
As for simply using your dictionary of word to word translations - that probably won't work to well (and is probably wrong). The same word does not translate to the same word every time in a different language (although much more likely to happen in technical discussions). For instance, the word beautiful. There is not only more than one word I could assign it to in Korean (i.e. there is a choice), but sometimes I lose that choice, as in the sentence (that food is beautiful), where I don't mean the food looks good. I mean it tastes good, and my option of translations for that word changes. And this is a VERY common circumstance.
Another big problem is optimal translation. Something that human's are really bad at, and something that computers are much much worse at. Whenever I've proofread a document translated from another text to English, I can always see various ways to cut it much much shorter.
So although, with statistics, you would be able to work out a pretty good average ratio in length between translations, this will be far different than it would be were all translations to be optimal.
It seems simple enough - you just need to find out the ratios.
For each script, count the number of script characters and English words in your glossary and work out the ratio.
This can be augmented with the Japanese source documents assuming you can both detect which script a Japanese word is in and what the English equivalent phrase is in the translation. Otherwise you'll have to guesstimate the ratios or ignore this as source data,
Then, as you say, count the number of words in each script of your source text, do the multiplies, and you should have a rough estimate.
My (albeit tiny) experience seems to indicate that, no matter what the language, blocks of text take the same amount of printed space to convey equivalent information. So, for a large-ish block of text, you could assign a width count to each character in English (grab this from a common font like Times New Roman), and likewise use a common Japanese font at the same point size to calculate the number of characters that would be required.
Related
I recently read the UTF-8 Everywhere manifesto, a document arguing for handling text with UTF-8 by default. The manifesto argues that Unicode codepoints aren't a generally useful concept and shouldn't be directly interacted with outside of programs/libraries specializing in text processing.
However, some modern languages that use the UTF-8 default have built-in codepoint types, such as rune in Go and char in Rust.
What are these types actually useful for? Are they legacy from times before the meaninglessness of codepoints was broadly understood? Or is that an incomplete perspective?
Texts have many different meaning and usages, so the question is difficult to answer.
First: about codepoint. We uses the term codepoint because it is easy, it implies a number (code), and not really confuseable with other terms. Unicode tell us that it doesn't use the term codepoint and character in a consistent way, but also that it is not a problem: context is clear, and they are often interchangeable (but for few codepoints which are not characters, like surrogates, and few reserved codepoints). Note: Unicode is mostly about characters, and ISO 10646 was most about codepoints. So original ISO was about a table with numbers (codepoint) and names, and Unicode about properties of characters. So we may use codepoints where Unicode character should be better, but character is easy confuseable with C char, and with font glyphs/graphemes.
Codepoints are one basic unit, so useful for most of programs, e.g. to store in databases, to exchange to other programs, to save files, for sorting, etc. For this exact reasons program languages uses the codepoint as type. UTF-8 code units may be an alternative, but it would be more difficult to navigate (see a UTF-8 as a tape disk where you should read sequentially, and codepoint text as an hard disk where you can just in middle of a text). Not a 100% appropriate, because you may need some context bytes. If you are getting user text, your program probably do not need to split in graphemes, to do liguatures, etc. if it will just store the data in a database. Codepoint is really low level and so fast for most operations.
The other part of text: displaying (or speech). This part is very complex, because we have many different scripts with very different rules, and then different languages with own special cases. So we needs a series of libraries, e.g. text layout (so word separation, etc. like pango), sharper engine (to find which glyph to use, combining characters, where to put next characters, e.g. HarfBuzz), and a font library which display the font (cairo plus freetype). it is complex, but most programmers do not need special handling: just reading text from database and sent to screen, so we just uses the relevant library (and it depends on operating system), and just going on. It is too complex for a language specification (and also a moving target, maybe in 30 years things are more standardized). So it is complex, and with many operation, so we may use complex structures (array of array of codepoint: so array of graphemes): not much a slow down. Note: fonts have codepoint tables to perform various operation before to find the glyph index. Various API uses Unicode strings (as codepoint array, UTF-16, UTF-8, etc.).
Naturally things are more complex, and it requires a lot of knowledge of different part of Unicode, if you are trying to program an editor (WYSIWYG, but also with terminals): you mix both worlds, and you need much more information (e.g. for selection of text). But in this case you must create your own structures.
And really: things are complex: do you want to just show first x characters on your blog? (maybe about assessment), or split at words (some language are not so linear, so the interpretation may be very wrong). For now just humans can do a good job for all languages, so also not yet need to a supporting type in different languages.
The manifesto argues that Unicode codepoints aren't a generally useful concept and shouldn't be directly interacted with outside of programs/libraries specializing in text processing.
Where? It merely outlines advantages and disadvantages of code points. Two examples are:
Some abstract characters can be encoded by different code points; U+03A9 greek capital letter omega and U+2126 ohm sign both correspond to the same abstract character Ω, and must be treated identically.
Moreover, for some abstract characters, there exist representations using multiple code points, in addition to the single coded character form. The abstract character ǵ can be coded by the single code point U+01F5 latin small letter g with acute, or by the sequence <U+0067 latin small letter g, U+0301 combining acute accent>.
In other words: code points just index which graphemes Unicode supports.
Sometimes they're meant as single characters: one prominent example would be € (EURO SIGN), having only the code point U+20AC.
Sometimes the same character has multiple code-points as per context: the dollar sign exists as:
﹩ = U+FE69 (SMALL DOLLAR SIGN)
＄ = U+FF04 (FULLWIDTH DOLLAR SIGN)
💲 = U+1F4B2 (HEAVY DOLLAR SIGN)
Storage wise when searching for one variant you might want to match all 3 variants instead on relying on the exact code point only.
Sometimes multiple code points can be combined to form up a single character:
á = U+00E1 (LATIN SMALL LETTER A WITH ACUTE), also termed "precomposed"
á = combination of U+0061 (LATIN SMALL LETTER A) and U+0301 (COMBINING ACUTE ACCENT) - in a text editor trying to delete á (from the right side) will mostly result in actually deleting the acute accent first. Searching for either variant should find both variants.
Storage wise you avoid to need searching for both variants by performing Unicode normalization, i.e. NFC to always favor precombined code points over two combined code points to form one character.
As for homoglyphs code points clearly distinguish the contextual meaning:
A = U+0041 (LATIN CAPITAL LETTER A)
Α = U+0391 (GREEK CAPITAL LETTER ALPHA)
А = U+0410 (CYRILLIC CAPITAL LETTER A)
Copy the greek or cyrillic character, then search this website for that letter - it will never find the other letters, no matter how similar they look. Likewise the latin letter A won't find the greek or cyrillic one.
Writing system wise code points can be used by multiple alphabets: the CJK portion is an attempt to use as few code points as possible while supporting as many languages as possible - Chinese (simplified, traditional, Hong Kong), Japanese, Korean, Vietnamese:
今 = U+4ECA
入 = U+5165
才 = U+624D
Dealing as a programmer with code points has valid reasons. Programming languages which support these may (or may not) support correct encodings (UTF-8 vs. UTF-16 vs. ISO-8859-1) and may (or may not) correctly produce surrogates for UTF-16. Text wise users should not be concerned about code points, although it would help them distinguishing homographs.
I have some unique codes that are generated from strings (ex: website host names) in various independent components of my application.
These codes are meant to be used by machines only so i would like to keep them as short as possible.
The below algorithm would be applied to every word in the string. The output words would be concatenated with a dash to generate the unique code.
The current algorithm I have used:
- Skip word if length is less than 6
- Leave first character as is
- Remove every wowel in the word from the second character onwards
architectural digest eu => archtctrl-dgst-eu
arizona foothills magazine => arzn-fthlls-mgzn
Is there a better way to shorten an English word leaving it as recognisable as possible to a human reader?
The output should be deterministic and produce the same shortened version whenever it is run on the same input.
A good algorithm should also minimise the number of clashes for similarly spelt words.
I have some unique codes that are generated from strings
I am afraid that is not true. There are many English words that will reduce to the same 'code word' when stripped of their vowels. For example, 'leaving' -> 'living' Given, this is fairly rare, it could still cause issues.
How important is it that these 'code words' remain human-readable if as you say, they are meant to be used by machines only? If its not that important, I'd suggest looking into some simpler compression algorithms like Huffman Coding or LZW Compression. Then if the user needs to see the translation of the code word, just uncompress it.
If you must keep it human-readable, I'm not sure that there is much more you can do to shorten it. You could take a look at specific latin + greek roots, and determine if you can shorten those any more by hand, and then just substitute those out automatically.
Alternatively, you could turn to a phonetic approach. Automatically search the pronunciation of the word, and then see if that is any shorter (or itself can be compressed, taking 'cee' to 'C', or 'kay' to 'K'). This would be much more time and CPU intensive, but its still an option if you really, really need short but yet readable codes.
What you're generating sounds like what's called a "slug". There are many libraries to handle this for blogs or site generators that should suit your purposes. Here's a usage example from a Python library called slugify:
txt = "___This is a test ---"
r = slugify(txt)
self.assertEqual(r, "this-is-a-test")
Slug libraries generally work like this:
replacing non-ascii linguistic characters via a mapping (ex: 影師嗎 -> ying-shi-ma)
replace accented latin letters with ascii equivalents via a mapping (ex: C'est déjà l'été. -> c-est-deja-l-ete)
remove beginning and trailing spaces/punctuation
convert remaining spaces and punctuation to dashes, collapsing multiple dashes in a row to a single dash
If you want to make slugs shorter you could remove vowels or, more simply, use a maximum length.
I have some text generated by some lousy OCR software.
The output contains mixture of words and space-separated characters, which should have been grouped into words. For example,
Expr e s s i o n Syntax
S u m m a r y o f T e r minology
should have been
Expression Syntax
Summary of Terminology
What algorithms can group characters into words?
If I program in Python, C#, Java, C or C++, what libraries provide the implementation of the algorithms?
Thanks.
Minimal approach:
In your input, remove the space before any single letter words. Mark the final words created as part of this somehow (prefix them with a symbol not in the input, for example).
Get a dictionary of English words, sorted longest to shortest.
For each marked word in your input, find the longest match and break that off as a word. Repeat on the characters left over in the original "word" until there's nothing left over. (In the case where there's no match just leave it alone.)
More sophisticated, overkill approach:
The problem of splitting words without spaces is a real-world problem in languages commonly written without spaces, such as Chinese and Japanese. I'm familiar with Japanese so I'll mainly speak with reference to that.
Typical approaches use a dictionary and a sequence model. The model is trained to learn transition properties between labels - part of speech tagging, combined with the dictionary, is used to figure out the relative likelihood of different potential places to split words. Then the most likely sequence of splits for a whole sentence is solved for using (for example) the Viterbi algorithm.
Creating a system like this is almost certainly overkill if you're just cleaning OCR data, but if you're interested it may be worth looking into.
A sample case where the more sophisticated approach will work and the simple one won't:
input: Playforthefunofit
simple output: Play forth efunofit (forth is longer than for)
sophistiated output: Play for the fun of it (forth efunofit is a low-frequency - that is, unnatural - transition, while for the is not)
You can work around the issue with the simple approach to some extent by adding common short-word sequences to your dictionary as units. For example, add forthe as a dictionary word, and split it in a post processing step.
Hope that helps - good luck!
Tesseract OCR engine sometimes outputs text that has no meaning, i want to design an algorithm that neglects any text or word that has no meaning, below is some sort of output text that i want to neglect,my simple solution is to count the words in the recognized text that's separated by " " and the text which has too many words will be garbage(Hint: i'm scanning images which at most will contains 40 words) any idea will be helpful,thanks.
wo:>"|axnoA1wvw\
ldflfig
°J!9O‘ !P99W M9N 6 13!-|15!Cl ‘I-/Vl
978 89l9 Z0 3+ 3 'l9.l.
97 999 VLL lLOZ+ 3 9l!q°lN
wo0'|axno/(#|au1e>1e: new;
1=96r2a1ey\1 1uauud0|e/\e(]
|8UJB){ p8UJL|\7'
Divide the output text into words. Divide the words into triples. Count the triple frequencies, and compare to triple frequencies from text of a known-good text corpus (EG all the articles from some mailing list discussing what you intend to OCR, minus the header lines).
When I say "triples", I mean:
whe, hen, i, say, tri, rip, ipl, ple, les, i, mea, ean
...so "i" has a frequency of 2 in this short example, while the others are all frequency 1.
If you do a frequency count of each of these triples for a large document in your intended language, it should become possible to be reasonably accurate in guessing whether a string is in the same language.
Granted, it's heuristic.
I've used a similar approach for detecting English passwords in a password changing program. It worked pretty well, though there's no such thing as a perfect "obvious password rejecter".
Check the words against a dictionary?
Of course, this will have false-positives for things like foreign-phrases or code. The problem in general is intractable (ex. is this code or gibberish? :) ). The only (nearly) perfect method would be to use this as a heuristic to flag certain sections for human review.
I want to find (not generate) 2 text strings such that, after removing all non letters and ucasing, one string can be translated to the other by simple substitution.
The motivation for this comes from a project I known of that is testing methods for attacking cyphers via probability distributions. I'd like to find a large, coherent plain text that, once encrypted with a simple substitution cypher, can be decrypted to something else that is also coherent.
This ends up as 2 parts, find the longest such strings in a corpus, and get that corpus.
The first part seems to me to be amiable to some sort of attack with a B-tree keyed off the string after a substitution that makes the sequence of first occurrences sequential.
HELLOWORLDTHISISIT
1233454637819a9b98
A little optimization based on knowing the maximum value and length of the string based on each depth of the tree and the rest is just coding.
The Other part would be quite a bit more involved; how to generate a large corpus of text to search? some kind of internet spider would seem to be the ideal approach as it would have access to the largest amount of text but how to strip it to just the text?
The question is; Any ideas on how to do this better?
Edit: the cipher that was being used is an insanely basic 26 letter substitution cipher.
p.s. this is more a thought experiment then a probable real project for me.
There are 26! different substitution ciphers. That works out to a bit over 88 bits of choice:
>>> math.log(factorial(26), 2)
88.381953327016262
The entropy of English text is something like 2 bits per character at least. So it seems to me you can't reasonably expect to find passages of more than 45-50 characters that are accidentally equivalent under substitution.
For the large corpus, there's the Gutenberg Project and Wikipedia, for a start. You can download an dump of all the English Wikipedia's XML files from their website.
I think you're asking a bit much to generate a substitution that is also "coherent". That is an AI problem for the encryption algorithm to figure out what text is coherent. Also, the longer your text is the more complicated it will be to create a "coherent" result... quickly approaching a point where you need a "key" as long as the text you are encrypting. Thus defeating the purpose of encrypting it at all.