Tag Archives: Orthography

Proposal revisited

I’ve written before about using WeSay to collect language data, and at one point I even wrote up a proposal for features I think it would be nice to have there –specifically targeted at orthography development, but the same tools could be used for spell checking (lining up similar profile words, that is, not coming up with a list of correctly spelled words). Anyway, it doesn’t look like that proposal is going anywhere, so I thought I’d give it another try.

The Basic Problem

  1. I’m working in an increasingly large number of languages (five people representing three languages were in my office most of this morning), and I’m looking to see that trend continue (looking to the 60 unwritten languages in eastern DRC).
  2. I am one person, and can only work on one language at a time (however often the language may change throughout the day).
  3. The best tool we have for sorting and manipulating large amounts of lexical data (FLEx) is great at what it does, but is inaccessible to most of the people I work with (it was made for linguists, after all. :-))
  4. So I am left with doing all the work with each guy in FLEx (see #2), or else finding a tool that can be more easily used by the people in #1.

Getting from Data collection to an Orthography

For a number of tasks (word collection), WeSay does exactly what I want. Since most of these people are touching a computer for the first time, let’s get rid of as much of the complexity and room for error as possible. Do one thing at a time, in a constrained environment. But once I’ve collected a wordlist in WeSay, I still need to

  1. Parse the roots out of the forms collected (I can collect plural forms in WeSay now, but we still need to get the full word forms into a field that should be full word forms (e.g., <citation>), and just roots in the lexical-unit field. In case it isn’t obvious, a lot of the phonological analysis depends on the structure of the root — the first root consonant is more important to our analysis that the first word consonant, so we need to be able to sort/filter on it.
  2. Sort and filter the word forms, so we see just one kind of thing at a time (we don’t want to see if kupaka and kukapa have the same ‘p’, since the difference in word position might cause such a difference that is irrelevant to the phonemic system (English speakers pronounce p’s differently in different environments, but use one letter for them all — did you know?).
  3. Go through each controlled list of words, to see where the current writing system (we use a national language to get us started) is not making enough distinctions, and where it is making too many.
  4. Mark changes on the appropriate words, returning the corrected information to the database.

While FLEx can handle all these tasks fine (however slowly at times), if I’m going to help other people move forward in their own language development, I need to find another tool (or tool set). I have got these guys quite happily working in WeSay (yes, there are kinks, but still we all are often working 3-4 times as fast as if I was typing everything myself, since there are more of us typing), but I need to get ready for these next steps, in a way that allows us to build on this momentum, rather than tell everyone but one team to go home until I have more time.

An Attempt

So I’ve been playing with XForms lately (for a lot of other reasons), and I’ve toyed with the idea of getting at and manipulating the LIFT file by another engine. The idea of being able to write a simple form to control the complexity of the underlying XML, and to manipulate it and save back to XML, was very exciting. I even have one form (for collecting noun class permutation examples) deployed. Then I read about the post describing the death of the Firefox XForms extension, and I thought surely, there must be a better way to do this, and I’m sure someone out there knows what it is. So I’ll spend some time outlining exactly what I’d like to see, and maybe someone will know what to do to make it happen.

A Proposal

I took some screenshots of some xforms I did, displayed in firefox. Here’s the first form, for parsing roots (Havu is the name of the language I’m using to test this, and one of the next who will be looking for this to work):

The important aspects are

  1. The prefix of the original citation form (sg here), which filters through the database, allowing us to work on words that are (probably) just of one noun class at a time.
  2. A number of possible plural prefixes, to control the potential output forms
  3. The originally input word form, with gloss (where present), to clearly identify each word.
  4. A number of buttons which allow a non-linguist to simply push a button to input the plural form and parse the root (probably including a “none of the above,” which would skip processing for this word).
  5. Under the hood processing which would, on a click:
  1. copy the original form into a citation form field in the database (potentially a new node in the lift XML),
  2. remove the prefix from the original form, and
  3. input the plural form into a field for the plural form (again, potentially a new node in the lift XML).

It would probably be better to show the user one word at a time, rather than the list in this screen-shot, but I included it all here, to show how the removal of the prefix, and the application of the new prefix, would need to apply to the form of each entry. Also, it would be nice if the form could be adapted for suffixing languages.
What I had so far for an example trigger (in XForms) would be

<xf:trigger><xf:label><xf:output value=”concat(‘mo’, substring(lexical-unit/form[@lang=’hav’]/text[starts-with(.,
‘aka’)], 4))” /></xf:label>
<xf:action ev:event=”DOMActivate”>
<xf:insert context=”.” origin=”instance(‘init’)/citation” />
<xf:setvalue ref=”./citation/form/text” value=“lexical-unit/form[@lang=’hav’]/text[starts-with(., ‘aka’)]”/>
<xf:setvalue ref=“lexical-unit/form[@lang=’hav’]/text[starts-with(., ‘aka’)]” value=“substring(lexical-unit/form[@lang=’hav’]/text[starts-with(., ‘aka’)], string-length(‘aka’) +1)”/>
<xf:send submission=”Save”/>

As you can see, the value of the prefix is hard-coded here, since I haven’t been able to get variables to work. also, the setvalue expressions don’t really behave (neither node-creation, not setting the right value for an existing node). It’s hard to tell what is a limitation of XForms, and what is a limitation of the Firefox extension –I tried another XForms renderer, but no luck so far… Needless to say, this is not what I do best, so help, anyone?
The next form I’d like sets ATR values for whole words (usually the harmony around here is fairly strict, so it would help with a lot, but not all, vowel questions):

This form is similar to the above, in that I’m looking for a simple regular expression (or a more complicated on, if possible. :-)) to control the data we’re looking at at once, and a binary choice for which vowel group the word belongs to (showing the new word form on the button). A choice of one or another would set the word form accordingly.
Same caveats above about the list of words on a page, and should probably have a “neither” button for when a word doesn’t obey strict vowel harmony, which would bypass processing for that word.
The trigger I had so far (for the -ATR button, reverse for the +ATR) was

<xf:trigger><xf:label><xf:output value=”translate(lexical-unit/form[@lang=’hav’]/text, ‘aeiou’, ‘aɛɨɔʉ’)”/>(-ATR)</xf:label>
<xf:action ev:event=”DOMActivate”>
<xf:setvalue context=”.” value=”translate(lexical-unit/form[@lang=’hav’]/text, ‘aeiou’, ‘aɛɨɔʉ’)”></xf:setvalue>
<xf:send submission=”Save”/>

The translation includes a>a, but could be modified depending on what /a/ does in a given language (especially if there are 10 vowels).
The third and final form, where we would spend the bulk of our time, might look like this:

Here we have:

  1. The ubiquitous regex to control the data we’re looking at.
  2. The letter that is being evaluated (we’re only analyzing one written form, be it digraph or not, at a time).
  3. The position to find that letter (something like first or second in the root, would probably be good enough).
  4. The options for replacing it (buttons labeled with the new forms), in case a word in the list uses a sound that doesn’t sound the same as the sound in that position in the rest of the words in the list.

Again, a choice of new word form would write the new form to the database (at which point the word would likely disappear from the list, until the regexp matched the new form of that word). It would be nice if the same replacement could be made in the citation and plural forms, presuming the same sounds and written letters would apply. I’m not sure if it would be necessary to devise two forms, one for consonants, and another for vowels. It would depend (at least) on how the regex worked, since the underlying principle is the same for consonants and vowels — look at one thing in one position at a time, and mark each one as the same or different, compared to the other things on the list.


Some things we would need to make this tool useful:

  1. Read and modify LIFT format in a predictable and non-destructive way (making all and only the changes we’re looking for, to the fields we’re looking at, leaving everything else alone). This is the format we’re keeping all our lexical data in, and we need to play nice with a number of other programs that use the same data structure standard.
  2. Use of regular expressions. For the root parsing tool, a simple prefix (or suffix) filter would do, but for the others it would be nice to be able to constrain syllable type, as well as position in the word (i.e., kupapata should not appear on the same list as kupapa, even if we’re looking at first ‘p’ in the root, since the second is a longer root). In FLEx, I use expressions like these (more included below), though a simpler format could do, if that kind of power were not possible. Ideally, these expressions would be put into a config file, and the user would only see the label (I have these all done, and can come up with more if needed).
  3. Cross-platform. We run most of our work on BALSA, so it would need to be able to run on at least Linux, which provides BALSA’s OS.
  4. Simple UI. It is probably not possible to overstate computer illiteracy we are dealing with here. People are eager to learn, and often capable to learn, but the less training we need, and the less room a given task has to screw everything else up, the better (WeSay‘s UI is a great model).
  5. Shareable. Even if not open sourced, any tool we might use here needs to be legally put on every computer we and our colleagues use, and they don’t often have the money or access to internet to buy licenses.
  6. Supportable. As hard as we try to keep the possibility of errors out of our workplace, it happens. Today we had two technological problems, each of which required non-significant amounts of my time. If I weren’t here (or if the problems had been beyond me), the teams would have been stuck. The simpler and more accessible (or absolutely error free!) the technology is under the hood, the more likely someone local will be able to deal with problems that arise in a timely manner.

Anyway, there it is.

Examples of Useful Regular Expressions for Filtering Lexical Data

The following are output by a script, which takes as input the kinds of graphs (e.g., d, t, ng’, and ngy) used in a given language’s writing system. For instance, these expressions do not allow ‘rh’ as a single consonant, but those I did for another language does. Similarly, these are based on ten particular vowel letters, which could also be changed for a given language.


(all CVCV –short vowels only)


(all CVCV –long vowels and dypthongs OK)


(all CVCV with C1=C2 –counting prenasalization)


(all CVCV with V1=V2 –no long V’s or dypthongs)


(all CVCV with V1=V2=a)

Using computers to Help the Computer Illiterate Develop their Language

I’ve been working with orthography development a bit, and it has been a major challenge getting all the different pieces of the work to fit together well. One of the main divides I see in work is between people who would use computers exclusively, and others would who use them not at all (during the research process at least). I have never felt comfortable in either camp, in part because I am of the computer generation, but also because I have seen a lot that paper and pencil methodology has to offer. Perhaps the most relevant point to our work is that computers are inaccessible to most of our national colleagues. Which means that if I’m doing everything on a computer, I’m doing it by myself. Or I’m teaching people things I started learning in the third grade (they know what the 0/1 symbol is from cell phones, but other than that, I’m usually starting from scratch).
Fortunately for me, there are people working on making linguistic computer work more accessible to the less computer literate, so we can take advantage of computers, without pushing our national colleagues out of the work. One bright shining example is WeSay. Its interface is straightforward and simple. Think of a word and type it in. Give it a short meaning. Next word. Later, you can go back and add longer meanings, other senses, etc. You can work through a wordlist, or use semantic domains — both great ways to help people think of new words to put in their dictionary. I had someone working on it for several days, to bring his wordlist up to over 2,000 words, and we were both quite happy with how it went. I didn’t realize just how happy I should have been until I had the same guy do some other tasks on the same computer (I think it was still just typing, but in openoffice). Things immediately bogged down, and I was constantly needed to fix something.
So here’s my problem: I really like dictionaries, and I think a dictionary is a backbone to any other work done in a language. And it just doesn’t make sense to write a dictionary on pen and paper. But the majority of my time is developing writing systems, which is better done as a community process (i.e., not everyone standing around watching me use a computer). The first thing I do is collect a wordlist, which I eventually make the beginnings of a dictionary. But that dictionary is going to need to be put in a database in a standardized writing system, or it will be a mess. So writing system development and lexicography go best together, but how?
Constance Kutsch Lojenga (among others) has developed a participatory linguistic research methodology, which is good at bringing speakers of a language into the language discovery process from the beginning (c.f., “Participatory Research in Linguistics. ”Notes on Linguistics Vol. 73(2):13-27. Dallas, TX: SIL. 1996.). Language community members see the sound distinctions in their language (at about) the same time I do, and we get to make writing system decisions as a group, given the linguistic facts we have observed together. The basic discovery process puts two words together, and asks, “is the sound in question in these two words the same, or different?” If they are the same, they are put in the same pile, if different, in different piles. There is, of course, lots of background work to be done (like sorting words by syllable profiles, so we’re looking at the same position in the same type of word at a time), but we attempt to make the discovery process itself as attainable as possible, and I have seen it work well.
Which brings me to my dilemma. This methodology as currently conceived uses words written on paper, which are sorted as a group. Which means, that if I use WeSay to collect a wordlist, I (or some other computer savvy person) need to export that wordlist into a format that will print onto paper that can be cut into cards (not hard with mailmerge, and document templates, but it is work), and then after the sorting process, all the information gained (e.g., “fapa” really should be spelled “paba”) needs to be put back into the database — or else it will just remain on those cards. Even if we write up a summary of selected cards in a report, the wordlist/dictionary won’t improve, unless we can get the information off those cards, and back into the computer — which again is not hard, but it is time consuming, and prone to error.
Which got me thinking, would there be a way to simplify the round-trip, and make the same/different decisions in a way that the spelling change information would be immediately returned to the database? As nice as the simple card-sorting interface is, if we could make something (at least nearly) as simple on a computer, I know our colleagues would be up to it. If we could make it simple. Which got me thinking about orthography and WeSay.
Anyway, I’ve written up some thoughts on how this might work practically, which you can find here.

Wesay Orthography Proposal

I think it would be helpful to language communities to have an orthography development/checker tool in WeSay. For why I think that and what I’d want to do with it, see this other post. Here’s what I would like to see:
Making the task simple for the user will necessarily require other complexities. We would need a config page, that would specify which words we’re looking for. It would basically be a filter for
1. Part of speech (maybe just from a list of noun, verb, and other)
2. Syllable type (e.g., px-CVCV, px-CVC, etc. –I’m not sure how hard this would be)
2b. position in the root: (C1, C2, V1, V2)
3. Consonants [] or Vowels [] (we just look at one or the other at a time)
4. Consonants available (for later selection by user)
5. Vowels available (for later selection by user)
6. Syllable types available (Options to choose from for dropdown in #2. For ortho development, this could contain just one or two canonical types; for full dictionary checking it would require more. I’m not sure how savvy WeSay could be at this point. It might be wise to split this into two groups, one for nouns, and another for verbs –or more.)

Based on the above information, the user is asked which letter to verify, and then would be presented with a “Verification” page where he would decide whether the sound in the position in question on each word is the same (or not) as others that are marked with the same letter (in the same position of the same types of words).
At the top of the page would be a new word, with LWC glosses and a picture, if available, and a list of words below it (all of which are filtered according to the settings on the config page, so we’re only looking at sounds/letters in one part of the same type of word) If it were simple to bold the letter in question in each word, that would be great, but not necessary to the task. In any case, it would be good to prominently display the letter under investigation, and more (very?) subtly the part of speech, syllable profile and root position. At the top of the page are two buttons: “Yes” and “No,” and at the bottom of the page are “Recheck” and “Verification OK/Next Letter”
The user task is to understand and pronounce the word at the top of the page, and decide if it belongs with the rest. If it does, he clicks “Yes”; otherwise, “No.” Once a button is clicked, another word is presented (If a mistake is made it will be sorted out later). Once the user has gone through all the input words and he is satisfied that the list of words all belong in the same group (if he isn’t sure, “Recheck” would redo the list –with just the “Yes” words), he clicks “Verification OK/Next Letter” and the following is done behind the scenes:
1. the letter (e.g., ‘p’, ‘b’, or ‘f’) in the place in question (e.g., first consonant) is changed to the letter being verified (e.g., ‘p’) for each word on the “Yes” list, if it isn’t already that letter.
2. those words are marked as verified for that letter (Is this possible? We could track progress elsewhere, if need be).
The user is then returned to the dialogue asking which letter to compare (next, and given the option to quit/pause). The same page is presented, but with words that had been taken out of the last group (i.e., “No” was clicked) along with words with the new letter, and the process repeats. If at any time the user terminates the task, it would be nice if the words that had been put in a box they might not belong in (i.e., marked “No” to ‘p,’ but not “Yes” to anything else yet) could be marked as unverified orthography (for that letter?). Then, when this task is started up again, it can start where it was left off.

At any point the user says, “give me the words with letter ‘x’,” he will get words that have been verified, and words that didn’t belong somewhere else (unless there were no “No” words on the last run). Thus, if there is a word that was wrongly put into a group (bad user input), a letter can always be re-verified (One can go back and click “No” on a word that one had just accidentally clicked “Yes” on). In any case, a spelling/letter is not corrected without comparing it to a list of words using the same letter in the same place of the same type of word. (I’m presuming good/new/consistent orthographies here, not ones like English, where a list of correctly spelled words would be more appropriate.)
Occasionally the process would need to be stopped to modify words that had been wrongly indicated for part of speech, root or syllable profile –unless the user doesn’t mind continually hitting “No,” or there is some other way of excluding them from the sort (maybe “Yes,” “No,” and “Wrong Category”?)
Once all the letters have been gone through for a given syllable type/position and part of speech, the settings on the config page could be changed, and the next set of words could be begun. If we put all but the letter to investigate on a hidden config page, then this would be just a one page task. Getting through all the letters in a given position can take awhile, so changes to those config settings (other than the letter to verify) wouldn’t need to be done all that often.

I originally had a “sorting” page before the verification page, since that is the way we do it with cards, but I think we could use a simple binary sort, as is present in the “verification” page, from the beginning. With cards on a table, a given sort might come up with a number of different piles (i.e., if there were m’s, f’s, and ɸ’s mixed up with p’s and b’s, for some reason. Or more likely, if there was under-differentiated vowels, and five vowels become nine.) But I think in either case, a binary sort would do, it would just take a bit longer (a few more sorts) to get each record in it’s proper pile.

I’m not sure how feasible this idea is; it is probably more like a “purple elephant” wish, but it would be nice to have, if possible. Without any real knowledge of the inner workings of WeSay, I imagine that knowledge of CV structure might be the largest hurdle to this idea’s implimentation. Normally when I collect words, I collect a couple forms: sg/pl for nouns, and infinitive/imperative for verbs, or whatever two forms will give me root structure information (a bit of preliminary research may be necessary to know what will work/help). While I can add fields for plural, infinitive and root in WeSay, there is no task to “add forms”, so giving WeSay information on syllable structure would be a more consultant-oriented task, using “Dictionary Browse and Edit”. Unless we had “Add forms: plural” and “Add forms: imperative” and then “Add Root forms”:
Add forms: plural:
Add forms: imperative:
Add Root forms:
(in config:
noun root parsing fields to compare:
[ ] and
[ ]
verb root parsing fields to compare:
[ ] and
[ ])
field 1:
field 2:
root: (this might even be guessable?)

But there are probably other hurdles that I would be completely unaware of. Anyway, this is my idea.

Actually, looking back at our proceedures, we would like an ability to filte on words with V1=V2 (at least), and also C1=C2, if possible.
Then normally we would look at all vowels in V2 position with V1=a (for instance), then V1=i, etc.
This makes the investivation process much more straightforward (paka is more clearly ‘a’ than paki), though perhaps it would impossibly complicate the innards of the task, were they ever doable.