A grammar of the isiZulu verb (present tense)

If you have read any of the blog posts on (automated) natural language generation for isiZulu, then you’ll probably agree with me that isiZulu verbs are non-trivial. True, verbs in other languages are most likely not as easy as in English, or Afrikaans for that matter (e.g., they made irregular verbs regular), but there are many little ‘bits and pieces’ ‘glued’ onto the verb root that make it semantically a ‘heavy’ element in a sentence. For instance:

  • Aba-shana ba-ya-zi-theng-is-el-an-a                izimpahla
  • Children   2.SC-Pres-8.OC-buyVR -C-A-R-FV 8.clothes
  • ‘The children are selling the clothes to each other’

The ba is the subject concord (~conjugation) to match with the noun class (which is 2) of the noun that plays the subject in the sentence (abashana), the ya denotes a continuous action (‘are doing something’ in the present), the zi is the object concord for the noun class (8) of the noun that plays the object in the sentence (izimpahla), theng is the verb root, then comes the CARP extension with is the causative (turning ‘buy’ into ‘sell’), and el the applicative and an the reciprocative, which take care of the ‘to each other’, and then finally the final vowel a.

More precisely, the general basic structure of the verb is as follows:

where NEG is the negative; SC the subject concord; T/A denotes tense/aspect; MOD the mood; OC the object concord; Verb Rad the verb radical; C the causative; A the applicative; R the reciprocal; and P the passive. For instance, if the children were not selling the clothes to each other, then instead of the SC, there would be the NEG SC in that position, making the verb abayazithengiselana.

To make sense of all this in a way that it would be amenable to computation, we—my co-author Langa Khumalo and I—specified the grammar of the complex verb for the present tense in a CFG using an incremental process of development. To the best of our (and the reviewer’s) knowledge, the outcome of the lengthy exercise is (1) the first comprehensive and precisely formulated documentation of the grammar rules for the isiZulu verb present tense, (2) all together in one place (cf. fragments sprinkled around in different papers, Wikipedia, and outdated literature (Doke in 1927 and 1935)), and (3) goes well beyond handling just one of the CARP, among others. The figure below summarises those rules, which are explained in detail in the forthcoming paper “Grammar rules for the isiZulu complex verb”, which will be published in the Southern African Linguistics and Applied Language Studies [1] (finally in print, yay!).

It is one thing to write these rules down on paper, and another to verify whether they’re actually doing what they’re supposed to be doing. Instead of fallible and laborious manual checking, we put them in JFLAP (for the lack of a better alternative at the time; discussed in the paper) and tested the CFG both on generation and recognition. The tests went reasonably well, and it helped fixing a rule during the testing phase.

Because the CFG doesn’t take into account phonological conditioning for the vowels, it generates strings not in the language. Such phonological conditioning is considered to be a post-processing step and was beyond the scope of elucidating and specifying the rules themselves. There are other causes of overgeneration that we did not get around to doing, for various reasons: there are rules that go across the verb root, which are simple to represent in coding-style notation (see paper) but not so much in a CFG, and rules for different types of verbs, but there’s no available resource that lists which verb roots are intransitive, which as monosyllabic and so on. We have started with scoping rules and solving issues for the latter, and do have a subset of phonological conditioning rules; so, to be continued… For now, though, we have completed at least one of the milestones.

Last, but not least, in case you wonder what’s the use of all this besides the linguistics to satisfy one’s curiosity and investigate and document an underresourced language: natural language generation for intelligent user interfaces in localised software, spellcheckers, and grammar checkers, among others.

 

References

[1] Keet, C.M., Khumalo, L. Grammar rules for the isiZulu complex verb. Southern African Linguistics and Applied Language Studies, (in print). Submitted version (the rules are the same as in the final version)

Aligning different relations: the case of part-whole relations—LDK2017

Despite the best intentions, I did not get around to writing a post on the paper that I presented last week at the First International Conference on Language, Data and Knowledge 2017, 19-20 June, Galway, Ireland, and now Paul Groth also ‘beat’ me to it writing a nice conference report of it. On the bright side, it is an opportunity to say upfront I really enjoyed the conference and look forward to the next edition in 2019. The ESWC’17 organisers might be slightly disappointed that there was no special track on the multilingual semantic web after all, but I did get the distinct impression that the LDK17 authors might just all have gambled on LDK17—an opportunity to binge two days on all things natural language & Semantic Web—rather than on one track at an overpriced conference (despite the allure of it being A-rated).

So, what was my paper about that could have been submitted to either? I ended up struggling—and solving—an issue with aligning OWL object properties that were not simple 1:1 mappings, in a similar scope as our ESWC17 paper (introduced here) [4], but then with too many complications. Complications were due to the different conceptualisations of part-whole relations and that one of the requirements was to solve what to do with an object property (relation, relationship) that does not have a neat, single, label, and therewith neither fitting with the common OWL modelling paradigm nor with the recently agreed-upon ontolex-lemon model for linguistic annotations.

The start of all this sounded nice and doable: we need to generate natural language for healthcare, using, e.g., SNOMED CT, in local languages in South Africa, focussing on the largest one, being isiZulu. Medical terminologies are riddled with part-whole relations, so we sought to address that one (simple existentials already having been solved), availing of a standard list of part-whole relations (e.g. [1]). That turned out to be a non-trivial exercise, but doable eventually [2]. What wasn’t addressed in [2] was that some ‘common’ part-whole relations, such as membership and containment, weren’t like that in isiZulu, at all. Moreover, it wasn’t just a language issue, but ontological as well. The LDK17 paper “Representing and aligning similar relations: parts and wholes in isiZulu vs English” [3] describes this in some detail.

Here’s a (simplified) list of (assumed to be) common part-whole relations, which takes into account both transitivity differences and domain and range:

Now here’s the one based on the isiZulu language and some ontological analysis of that:

That is: there are both generalisations—some distinctions are not being made—and specialisations—some distinctions are made here but not elsewhere. For instance, ‘musician is part of some orchestra’ and ‘heart is part of some human’ (or vv.) is all done and described in the same way (ingxenye ‘part of’ and SC+CONJ for ‘has part’ [more about that below]). Yet, there is a difference between an individual (e.g., a voter) participating in some process and a collective (e.g., the electorate) participating in a process, or vv. The paper describes this more precisely, going into some detail regarding the differences in categories of domain and range and into the consequences on transitivity of mereological parthood.

The other ‘odd thing’—cf. current multilingual Semantic Web assumptions and technologies, that is—is that while the conceptualisation of ‘has part’ exists, it does not have a single label as in English (or in several other languages, such as heeft as deel), but it is dependent on the noun class of the noun of the class that play the part and play the whole in the relation. It combines the subject concord (~conjugation) of the noun class of the noun that plays the whole with a conjunction that is phonologically conditioned based on the first letter of the noun that plays the part; with verbalisation in the plural and three phonological cases, there are 18 possible strings all denoting ‘has part’. This still could be sorted with a language with inverses, provided the part-of direction has a name, like the ingxenye. This is not the case for containment, however. Instead of the relation (object property) having a name—be this a verb like ‘contained in’ or some noun phrase—it is the noun that plays the whole (the container, if you will) that gets modified. For instance, imvilophu ‘envelope’ and emvilophini denoting ‘contained in the envelope’, or, for individuals and locations, the city iTheku ‘Durban’ and eThekwini meaning ‘located in Durban’ (no typo—there’s some phonological conditioning I’m brushing over). While I have gotten used to such constructions, it generated some surprise among several attendees that one can have notions, concepts, views on or interpretations or descriptions of reality, that exist but do not have even one single string of text throughout to refer to regardless the context it is used.

The naming issue was solved by adding some arbitrary string as ‘name’ of the object property, and relating that to the function that verbalises that specific part-whole relation. The former issue, i.e., not all the same part-whole relations, required a bit more work, using ontology pattern alignments, by extending one correspondence pattern from the ODP catalogue and introducing a new one (see paper for the formal details), using the same broad framework of formalisation as proposed in [4].

All this was then implemented and aligned, and verified to not result in some unsatisfiable classes, object properties, or inconsistency (files). This also works in the isiZulu verbalisation tool we demo-ed at ESWC17 (described in the previous post) [5], all as part of the NRF-funded GeNI project.

Now, ideally, I already would have had the time to read the papers I flagged in my LDK17 conference notes with “check paper”. I haven’t yet due to end-of-semester tasks. So, on the basis of just a positive-seeming presentation, here are a few that are on the top of my list to check out first, for quite different reasons:

  • Interaction between natural language reading capabilities and math education, focusing on language production (i.e., ‘can you talk about it?’) [6], mainly because math education in South Africa faces a lot of problems. It also generated a lively discussion in the Q&A session.
  • The OnLiT ontology for linguistic [7] and LLODifying linguistic glosses [8] terminology (also: one of the two also won the best paper award).
  • Deep text generation, for it was looking at trying to address skewed or limited data to learn from [9], which is an issue we face when trying to do some NLP with most South African languages.

 

References

[1] Keet, C.M., Artale, A. Representing and Reasoning over a Taxonomy of Part-Whole Relations. Applied Ontology, 2008, 3(1-2):91-110.

[2] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. 9th International Natural Language Generation conference (INLG’16), September 5-8, 2016, Edinburgh, UK. ACL.

[3] Keet, C.M. Representing and aligning similar relations: parts and wholes in isiZulu vs English. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 58-73.

[4] Fillottrani, P.R., Keet, C.M. Patterns for Heterogeneous TBox Mappings to Bridge Different Modelling Decisions. 14th Extended Semantic Web Conference (ESWC’17). Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017.

[5] Keet, C.M. Xakaza, M., Khumalo, L. Verbalising OWL ontologies in isiZulu with Python. 14th Extended Semantic Web Conference (ESWC’17). Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017. (demo paper)

[6] Crossley, S., Kostyuk, V. Letting the genie out of the lamp: using natural language processing tools to predict math performance. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 330-342.

[7] Klimek, B., McCrae, J.P., Lehmann, C., Chiarcos, C., Hellmann, S. OnLiT: and ontology for linguistic terminology. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 42-57.

[8] Chiarcos, C., Ionov, M. Rind-Pawlowski, M., Fäth, C., Wichers Schreur, J., Nevskaya. I. LLODifying linguistic glosses. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 89-103.

[9] Dethlefs N., Turner A. Deep Text Generation — Using Hierarchical Decomposition to Mitigate the Effect of Rare Data Points. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge LDK 2017. Springer LNAI vol 10318, 290-298.

Our ESWC17 demos: TDDonto2 and an OWL verbaliser for isiZulu

Besides the full paper on heterogeneous alignments for 14th Extended Semantic Web Conference (ESWC’17) that will take place next week in Portoroz, Slovenia, we also managed to squeeze out two demo papers. You may already know of TDDonto2 with Kieren Davies and Agnieszka Lawrynowicz, which was discussed in an earlier post that has been updated with a tutorial video. It now has a demo paper as well [1], which describes the rationale and a few scenarios. The other demo, with Musa Xakaza and Langa Khumalo, is new-new, but the regular reader might have seen it coming: we finally managed to link the verbalisation patterns for certain Description Logic axiom types [2,3] to those in OWL ontologies. The tool takes as input an ontology in isiZulu and the verbalisation algorithms, and out come the isiZulu sentences, be this in plain text for further processing or in a GUI for inspection by a domain expert [4]. There is a basic demo-screencast to show it’s all working.

The overall architecture may be of interest, for it deviates from most OWL verbalisers. It is shown in the following figure:

For instance, we use the Python-based OWL API Owlready, rather than a Java-based app, for Python is rather popular in NLP and the verbalisation algorithms may be used elsewhere as well. We made more such decisions with the aim to make whatever we did as multi-purpose usable as possible, like the list of nouns with noun classes (surprisingly, and annoyingly, there is no such readily available list yet, though isizulu.net probably will have it somewhere but inaccessible), verb roots, and exceptions in pluralisation. (Problems for integrating the verbaliser with, say, Protégé will be interesting to discuss during the demo session!)

The text-based output doesn’t look as nice as the GUI interface, so I will show here only the GUI interface, which is adorned with some annotations to illustrate that those verbalisation algorithms in the background are far from trivial templates:

For instance, while in English the universal quantification is always ‘Each’ or ‘All’ regardless the named class quantified over, in isiZulu it depends on the noun class of the noun that is the name of the OWL class. For instance, in the figure above, izingwe ‘leopards’ is in noun class 10, so the ‘Each/All’ is Zonke, amavazi ‘vases’ is in noun class 6, so ‘Each/All’ then becomes Onke, and abantu ‘people’/’humans’ is in noun class 2, making Bonke. There are 17 noun classes. They also determine the subject concords (SC, alike conjugation) for the verbs, with zi- for noun class 10, ­a- for noun class 6, and ba- for noun class 2, to name a few. How this all works is described in [2,3]. We’ve implemented all those algorithms and integrated the pluraliser [5] in it to make it work. The source files are available to check and play with already, you can do so and ask us during the ESWC17 demo session, and/or also have a look at the related outputs of the NRF-funded project Grammar Engine for Nguni natural language interfaces (GeNi).

 

References

[1] Davies, K. Keet, C.M., Lawrynowicz, A. TDDonto2: A Test-Driven Development Plugin for arbitrary TBox and ABox axioms. Extended Semantic Web Conference (ESWC’17), Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017. (demo paper)

[2] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2017, 51:131-157.

[3] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. 9th International Natural Language Generation conference (INLG’16), 5-8 September, 2016, Edinburgh, UK. Association for Computational Linguistics, 174-183.

[4] Keet, C.M. Xakaza, M., Khumalo, L. Verbalising OWL ontologies in isiZulu with Python. 14th Extended Semantic Web Conference (ESWC’17). Springer LNCS. Portoroz, Slovenia, May 28 – June 2, 2017. (demo paper)

[5] Byamugisha, J., Keet, C.M., Khumalo, L. Pluralising Nouns in isiZulu and Related Languages. 17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’16), Springer LNCS. April 3-9, 2016, Konya, Turkey.

Robot peppers, monkey gland sauce, and go well—Say again? reviewed

The previous post about TDDonto2 had as toy example a pool braai, which does exist in South Africa at least, but perhaps also elsewhere under a different name: the braai is the ‘South African English’ (SAE) for the barbecue. There are more such words and phrases peculiar to SAE, and after the paper deadline last week, I did finish reading the book Say again? The other side of South African English by Jean Branford and Malcolm Venter (published earlier this year) that has many more examples of SAE and a bit of sociolinguistics and some etymology of that. Anyone visiting South Africa will encounter at least several of the words and sentence constructions that are SAE, but probably would raise eyebrows elsewhere. Let me start with some examples.

Besides the braai, one certainly will encounter the robot, which is a traffic light (automating the human police officer). A minor extension to that term can be found in the supermarket (see figure on the right): robot peppers, being a bag of three peppers in the colours of red, yellow, and green—no vegetable AI, sorry. robotpeppers

How familiar the other ones discussed in the book are, depends on how much you interact with South Africans, where you stay(ed), and how much you read and knew about the country before visiting it, I suppose. For instance, when I visited Pretoria in 2008, I had not come across the bunny, but did so upon my first visit in Durban in 2010 (it’s a hollowed-out half a loaf of bread, filled with a curry) and bush college upon starting to work at a university (UKZN) here in 2011. The latter is a derogatory term that was used for universities for non-white students in the Apartheid era, with the non-white being its own loaded term from the same regime. (It’s better not to use it—all terms for classifying people one way or another are a bit of a mine field, whose nuances I’m still trying to figure out; the book didn’t help with that).

Then there’s the category of words one may know from ‘general English’, but are by the authors claimed to have a different meaning here. One is the sell-outs, which is “to apply particularly to black people who were thought to have betrayed their people” (p143), though I have the impression it can be applied generally. Another is townhouse, which supposedly has narrowed its meaning cf. British English (p155), but from having lived on the isles some years ago, it was used in the very same way as it is here; the book’s authors just stick to its older meaning and assume the British and Irish do so too (they don’t, though). One that indeed does fall in the category ‘meaning restriction’ is transformation (an explanation of the narrower sense will take up too much space). While I’ve learned a bunch of the ‘unusual’ usual words in the meantime I’ve worked here, there were others that I still did wonder about. For instance, the lay-bye, which the book explained to be the situation when the shop sets aside a product the customer wants, and the customer pays the price in instalments until it is fully paid before taking the product home. The monkey gland sauce one can buy in the supermarket is another, which is a sauce based on ketchup and onion with some chutney in it—no monkeys and no glands—but, I’ll readily admit, I still have not tried it due to its awful name.

There are many more terms described and discussed in the book, and it has a useful index at the end, especially given that it gives the impression to be a very popsci-like book. The content is very nicely typesetted, with news item snippets and aside-boxes and such. Overall, though, while it’s ok to read in the gym on the bicycle for a foreigner who sometimes wonders about certain terms and constructions, it is rather uni-dimensional from a British White South African perspective and the authors are clearly Cape Town-based, with the majority of examples from SA media from Cape Town’s news outlets. They take a heavily Afrikaans-influence-only bias, with, iirc, only four examples of the influence of, e.g., isiZulu on SAE (e.g., the ‘go well’ literal translation of isiZulu’s hamba kahle), which is a missed opportunity. A quick online search reveals quite a list of words from indigenous languages that have been adopted (and more here and here and here and here) such as muti (medicine; from the isiZulu umuthi) and maas (thick sour milk; from the isiZulu amasi) and dagga (marijuana; from the Khoe daxa-b), not to mention the many loan words, such as indaba (conference; isiZulu) and ubuntu (the concept, not the operating system—which the authors seem to be a bit short of, given the near blind spot on import of words with a local origin). If that does not make you hesitant to read it, then let me illustrate some more inaccuracies beyond the aforementioned townhouse squabble, which results in having to take the book’s contents probably with a grain of salt and heavily contextualise it, and/or at least fact-check it yourself. They fall in at least three categories: vocabulary, grammar, and etymology.

To quote: “This came about because the Dutch term tijger means either tiger or leopard” (p219): no, we do have a word for leopard: luipaard. That word is included even in a pocket-size Prisma English-Dutch dictionary or any online EN-NL dictionary, so a simple look-up to fact-check would have sufficed (and it existed already in Dutch before a bunch of them started colonising South Africa in 1652; originating from old French in ~1200). Not having done so smells of either sloppiness or arrogance. And I’m not so sure about the widespread use of pavement special (stray or mongrel dogs or cats), as my backyard neighbours use just stray for ‘my’ stray cat (whom they want to sterilise because he meows in the morning). It is a fun term, though.

Then there’s stunted etymology of words. The coconut is not a term that emerged in the “new South Africa” (pp145-146), but is transferred from the Americas where it was already in use for at least since the 1970s to denote the same concept (in short: a brown skinned person who is White on the inside) but then applied to some people from Central and South America [Latino/Hispanic; take your pick].

Extending the criticism also to the grammar explanations, the “with” aside box on pp203-204 is wrong as well, though perhaps not as blatantly obvious as the leopard and coconut ones. The authors stipulate that phrases like “Is So-and-So coming with?” (p203) is Afrikaans influence of kom saam “where saam sounds like ‘with’” (p203) (uh, no, it doesn’t), and as more guessing they drag a bit of German influence in US English into it. This use, and the related examples like the “…I have to take all my food with” (p204) is the same construction and similar word order for the Dutch adverb mee ‘with’ (and German mit), such as in the infinitives meekomen ‘to come with’ (komen = to come), meenemen ‘to take with’, meebrengen ‘to bring with’, and meegaan ‘to go with’. In a sentence, the mee may be separated from the rest of the verb and put somewhere, including at the end of the sentence, like in ik neem mijn eten mee ‘I take my food with’ (word-by-word translated) en komt d’n dieje mee? ‘comes so-and-so with?’ (word-by-word translated, with a bit of ABB in the Dutch). German has similar infinitives—mitkommen, mitnehmen, mitbringen, and mitgehen, respectively—sure, but the grammar construction the book’s authors highlight is so much more likely to come from Dutch as first step of tracing it back, given that Afrikaans is a ‘simplified’ version of Dutch, not of German. (My guess would be that the Dutch mee- can be traced back, in turn, to the German mit, as Dutch is a sort of ‘simplified’ German, but that’s a separate story.)

In closing, I could go on with examples and corrections, and maybe I should, but I think I made the point clear. The book didn’t read as badly as it may seem from this review, but writing the review required me to fact-check a few things, rather than taking most of it at face value, which made it turn out more and more mediocre than the couple of irritations I had whilst reading it.

Launch of the isiZulu spellchecker

launchspellchecker

Langa Khumalo, ULPDO director, giving the spellchecker demo, pointing out a detected spelling error in the text. On his left, Mpho Monareng, CEO of PanSALB.

Yesterday, the isiZulu spellchecker was launched at UKZN’s “Launch of the UKZN isiZulu Books and Human Language Technologies” event, which was also featured on 702 live radio, SABC 2 Morning Live, and e-news during the day. What we at UCT have to do with it is that both the theory and the spellchecker tool were developed in-house by members of the Department of Computer Science at UCT. The connection with UKZN’s University Language Planning & Development Office is that we used a section of their isiZulu National Corpus (INC) [1] to train the spellchecker with, and that they wanted a spellchecker (the latter came first).

The theory behind the spellchecker was described briefly in an earlier post and it has been presented at IST-Africa 2016 [2]. Basically, we don’t use a wordlist + rules-based approach as some experiments of 20 years ago did, nor a wordlist + a few rules of the now-defunct translate.org.za OpenOffice v3 plugin seven years ago, but a data-driven approach with a statistical language model that uses tri-grams. The section of the INC we used were novels and news items, so, including present-day isiZulu texts. At the time of the IST-Africa’16 paper, based on Balone Ndaba’s BSc CS honours project, the spell checking was very proof-of-concept, but it showed that it could be done and still achieve a good enough accuracy. We used that approach to create an enduser-usable isiZulu spellchecker, which saw the light of day thanks to our 3rd-year CS@UCT student Norman Pilusa, who both developed the front-end and optimised the backend so that it has an excellent performance.

Upon starting the platform-independent isiZulu_spellchecker.jar file, the English interface version looks like this:

zuspellopen

You can write text in the text box, or open a txt or docx file, which then is displayed in the textbox. Click “Run”. Now there are two options: you can choose to step-through the words that are detected as misspelled one at a time or “Show All” words that are detected as misspelled. Both are shown for some sample text in the screenshot below.

zuspellonessection

processing one error at a time

zuspellallsection

highlighting all words detected as very probably misspelled

Then it is up to you to choose what to do with it: correct it in the textbox, “Ignore once”, “Ignore all”, or “Add” the word to your (local) dictionary. If you have modified the text, you can save it with the changes made by clicking “Save correction”. You also can switch the interface from the default English to isiZulu by clicking “File – Use English”, and back to English via “iFayela – ulimi lesingisi”. You can download the isiZulu spellchecker from the ULPDO website and from the GitHub repository for those who want to get their hands on the source code.

To anticipate some possible questions you may have: incorporating it as a plugin to Microsoft word, OpenOffice/LibreOffice, and Mozilla Firefox was in the planning. The former is technologically ‘closed source’, however, and the latter two have a certain way of doing spellchecking that is not amenable to the data-driven approach with the trigrams. So, for now, it is a standalone tool. By design, it is desktop-based rather than for mobile phones, because according to the client (ULPDO@UKZN), they expect the first users to be professionals with admin documents and emails, journalists writing articles, and such, writing on PCs and laptops.

There was also a trade-off between a particular sort of error: the tool now flags more words as probably incorrect than it could have, yet it will detect (a subset of) capitalization, correctly, such as KwaZulu-Natal whilst flagging some of the deviant spellings that go around, as shown in the screenshot below.

zuspellkznThe customer preferred recognising such capitalisation.

Error correction sounds like an obvious feature as well, but that will require a bit more work, not just technologically, but also the underlying theory. It will probably be an honours project topic for next year.

In the grand scheme of things, the current v1 of the spellchecker is only a small step—yet, many such small steps in succession will get one far eventually.

The launch itself saw an impressive line-up of speeches and introductions: the keynote address was given by Dr Zweli Mkhize, UKZN Chancellor and member of the ANC NEC; Prof Ramesh Krishnamurthy, from Aston University UK, gave the opening address; Mpho Monareng, CEO of PanSALB gave an address and co-launched the human language technologies; UKZN’s VC Andre van Jaarsveld provided the official welcome; and two of UKZN’s DVCs, Prof Renuka Vithal and Prof Cheryl Potgieter, gave presentations. Besides our ‘5-minutes of fame’ with the isiZulu spellchecker, the event also launched the isiZulu National Corpus, the isiZulu Term Bank, the ZuluLex mobile-compatible application (Android and iPhone), and two isiZulu books on collected short stories and an English-isiZulu architecture glossary.

 

References

[1] Khumalo, L. Advances in developing corpora in African languages. Kuwala, 2015, 1(2): 21-30.

[2] Ndaba, B., Suleman, H., Keet, C.M., Khumalo, L. The Effects of a Corpus on isiZulu Spellcheckers based on N-grams. IST-Africa 2016. May 11-13, 2016, Durban, South Africa.

Relations with roles / verbalising object properties in isiZulu

The narratives can be very different for the paper “A model for verbalising relations with roles in multiple languages” that was recently accepted paper at the 20th International Conference on Knowledge Engineering and Knowledge management (EKAW’16), for the paper makes a nice smoothie of the three ingredients of language, logic, and ontology. The natural language part zooms in on isiZulu as use case (possibly losing some ontologist or logician readers), then there are the logics about mapping the Description Logic DLR’s role components with OWL (lose possible interest of the natural language researchers), and a bit of philosophy (and lose most people…). It solves some thorny issues when trying to verbalise complicated verbs that we need for knowledge-to-text natural language generation in isiZulu and some other languages (e.g., German). And it solves the matching of logic-based representations popularised in mainly UML and ORM (that typically uses a logic in the DLR family of Description Logic languages) with the more commonly used OWL. The latter is even implemented as a Protégé plugin.

Let me start with some use-cases that cause problems that need to be solved. It is well-known that natural language renderings of ontologies facilitate communication with domain experts who are expected to model and validate the represented knowledge. This is doable for English, with ACE in the lead, but it isn’t for grammatically richer languages. There, there are complications, such as conjugation of verbs, an article that may be dependent on the preposition, or a preposition may modify the noun. For instance, works for, made by, located in, and is part of are quite common names for object properties in ontologies. They all do have a dependent preposition, however, there are different verb tenses, and the latter has a copulative and noun rather than just a verb. All that goes into the object properties name in an ‘English-based ontology’ and does not really have to be processed further in ontology verbalisation other than beautification. Not so in multiple other languages. For instance, the ‘in’ of located in ends up as affixes to the noun representing the object that the other object is located in. Like, imvilophu ‘envelope’ and emvilophini ‘in the envelope’ (locative underlined). Even something straightforward like a property eats can end up having to be conjugated differently depending on who’s eating: when a human eats, it is udla in isiZulu, but for, say, a dog, it is idla (modification underlined), which is driven by the system of noun classes, of which there are 17 in isiZulu. Many more examples illustrating different issues are described in the paper. To make a long story short, there are gradations in complicating effects, from no effect where a preposition can be squeezed in with the verb in naming an OP, to phonological conditioning, to modifying the article of the noun to modifying the noun. A ‘3rd pers. sg.’ may thus be context-dependent, and notions of prepositions may modify the verb or the noun or the article of the noun, or both. For a setting other than English ontologies (e.g., Greek, German, Lithuanian), a preposition may belong neither to the verb nor to the noun, but instead to the role that the object plays in the relation described by the verb in the sentence. For instance, one obtains yomuntu, rather than the basic noun umuntu, if it plays the role of the whole in a part-whole relation like in ‘heart is part of a human’ (inhliziyo iyingxenye yomuntu).

The question then becomes how to handle such a representation that also has to include roles? This is quite common in conceptual data modelling languages and in the DLR family of DL languages, which is known in ontology as positionalism [2]. Bumping up the role to an element in the representation language—thus, in addition to the relationship—enables one to attach information to it, like whether there is a (deep) preposition associated with it, the tense, or the case. Such role-based annotations can then be used to generate the right element, like einen Betrieb ‘some company’ to adjust the article for the case it goes with in German, or ya+umuntu=yomuntu ‘of a human’, modifying the noun in the object position in the sentence.

To get this working properly, with a solid theoretical foundation, we reused a part of the conceptual modelling languages’ metamodel [3] to create a language model for such annotations, in particular regarding the attributes of the classes in the metamodel. On its own, however, it is rather isolated and not immediately useful for ontologies that we set out to be in need of verbalising. To this end, it links to the ‘OWL way of representing relations’ (ontologically: the so-called standard view), and we separate out the logic-based representation from the readings that one can generate with the structured representation of the knowledge. All in all, the simplified high-level model looks like the picture below.

Simplified diagram in UML Class Diagram notation of the main components (see paper for attributes), linking a section of the metamodel (orange; positionalist commitment) to predicates (green; standard view) and their verbalisation (yellow). (Source: [1])

Simplified diagram in UML Class Diagram notation of the main components (see paper for attributes), linking a section of the metamodel (orange; positionalist commitment) to predicates (green; standard view) and their verbalisation (yellow). (Source: [1])

That much for the conceptual part; more details are described in the paper.

Just a fluffy colourful diagram isn’t enough for a solid implementation, however. To this end, we mapped one of the logics that adhere to positionalism to one of the standard view, being DLR [4] and OWL, respectively. It equally well could have been done for other pairs of languages (e.g., with Common Logic), but these two are more popular in terms of theory and tools.

Having the conceptual and logical foundations in place, we did implement it to see whether it actually can be done and to check whether the theory was sufficient. The Protégé plugin is called iMPALA—it could be an abbreviation for ‘Model for Positionalism And Language Annotation’—that both writes all the non-OWL annotations in a separate XML file and takes care of the renderings in Protégé. It works; yay. Specifically, it handles the interaction between the OWL file, the positionalist elements, and the annotations/attributes, plus the additional feature that one can add new linguistic annotation properties, so as to cater for extensibility. Here are a few screenshots:

OWL’s arbeitetFuer ‘works for’ is linked to the relationship arbeiten.

OWL’s arbeitetFuer ‘works for’ is linked to the relationship arbeiten.

The prey role in the axiom of the impala being eaten by the ibhubesi.

The prey role in the axiom of the impala being eaten by the ibhubesi.

 Annotations of the prey role itself, which is a role in the relationship ukudla.

Annotations of the prey role itself, which is a role in the relationship ukudla.

We did test it a bit, from just the regular feature testing to the African Wildlife ontology that was translated into isiZulu (spoken in South Africa) and a people and pets ontology in ciShona (spoken in Zimbabwe). These details are available in the online supplementary material.

The next step is to tie it all together, being the verbalisation patterns for isiZulu [5,6] and the OWL ontologies to generate full sentences, correctly. This is set to happen soon (provided all the protests don’t mess up the planning too much). If you want to know more details that are not, or not clearly, in the paper, then please have a look at the project page of A Grammar engine for Nguni natural language interfaces (GeNi), or come visit EKAW16 that will be held from 21-23 November in Bologna, Italy, where I will present the paper.

 

References

[1] Keet, C.M., Chirema, T. A model for verbalising relations with roles in multiple languages. 20th International Conference on Knowledge Engineering and Knowledge Management EKAW’16). Springer LNAI, 19-23 November 2016, Bologna, Italy. (in print)

[2] Leo, J. Modeling relations. Journal of Philosophical Logic, 2008, 37:353-385.

[3] Keet, C.M., Fillottrani, P.R. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering, 2015, 98:30-53.

[4] Calvanese, D., De Giacomo, G. The Description Logics Handbook: Theory, Implementation and Applications, chap. Expressive description logics, pp. 178-218. Cambridge University Press (2003).

[5] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2016, in print.

[6] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. Proceedings of the 9th International Natural Language Generation conference 2016 (INLG’16), Edinburgh, Scotland, Sept 2016. ACL, 174-183.

Surprising similarities and differences in orthography across several African languages

It is well-known that natural language interfaces and tools in one’s own language are known to be useful in ICT-mediated communication. For instance, tools like spellcheckers and Web search engines, machine translation, or even just straight-forward natural language processing to at least ‘understand’ documents and find the right one with a keyword search. Most languages in Southern Africa, and those in the (linguistically called) Bantu language family, are still under-resourced, however, so this is not a trivial task due to the limited data and researched and documented grammar. Any possibility to ‘bootstrap’ theory, techniques, and tools developed for one language and to fiddle just a bit to make it work for a similar one will save many resources compared to starting from scratch time and again. Likewise, it would be very useful if both the generic and the few language-specific NLP tools for the well-resourced languages could be reused or easily adapted across languages. The question is: does that work? We know very little about whether it does. Taking one step back, then: for that bootstrapping to work well, we need to have insight into how similar the languages are. And we may be able to find that out if only we knew how to measure similarity of languages.

The most well-know qualitative way for determining some notion of similarity started with Meinhof’s noun class system [1] and the Guthrie zones. That’s interesting, but not nearly enough for computational tools. An experiment has been done for morphological analysers [2], with promising results, yet it also had more of a qualitative flavour to it.

I’m adding here another proverbial “2 cents” to it, by taking a mostly quantitative approach to it, and focusing on orthography (how things are written down) in text documents and corpora. This was a two-step process. First, 12 versions of the Universal Declaration of Human Rights were examined on tokens and their word length; second, because the UDHR is a quite small document, isiZulu corpora were examined to see whether the UDHR was a representative sample, i.e., whether extrapolation from its results may be justified. The methods, results, and discussion are described in “An assessment of orthographic similarity measures for several African languages” [3].

The really cool thing of the language comparison is that it shows clusters of languages, indicating where bootstrapping may have more or less success, and they do not quite match with Guthrie zones. The cumulative frequency distributions of the words in the UDHR of several languages spoken in Sub-Saharan Africa is shown in the figure below, where the names of the languages are those of the file names of the NLTK data kit that contains the quality translations of the UDHR.

Cumulative frequency distributions of the words in the UDHR of several languages spoken in Sub-Saharan Africa (Source: [3]).

Cumulative frequency distributions of the words in the UDHR of several languages spoken in Sub-Saharan Africa (Source: [3]).

The paper contains some statistical tests, showing that the bottom cluster are not statistically significantly different form each other, but they are from the ‘middle’ cluster. So, the word length distribution of Kiswahili is substantially different from that of, among others, isiZulu, in that it has more shorter words and isiZulu more longer words, but Kiswahili’s pattern is similar to that of Afrikaans and English. This is important for NLP, for isiZulu is known to be highly agglutinating, but English (and thus also Kiswahili) is disjunctive. How important is such a difference? The simple answer is that grammatical elements of a sentences get ‘glued’ together in isiZulu, whereas at least some of them are written as separate words in Kiswahili. This is not to be conflated with, say, German, Dutch, and Afrikaans, where nouns can be concatenated to form new words, but, e.g., a preposition is glued onto a noun. For instance, ‘of clay’ is ngobumba, contracting nga+ubumba with a vowel coalescence rule (-a + u- = -o-), which thus happens much less often in a language with disjunctive orthography. This, in turn, affects the algorithms needed to computationally process the languages, hence, the prospects for bootstrapping.

Note that middle cluster looks deceptively isolating, but it isn’t. Sesotho and Setswana are statistically significantly different from the others, in that they are even more disjunctive than English. Sepedi (top-most line) even more so. While I don’t know that language, a hypothetical example suffice to illustrate this notion. There is conjugation of verbs, like ‘works’ or trabajas or usebenza (inflection underlined), but some orthographer a while ago could have decided to write that separate from the verb stem (e.g., trabaj as and u sebenza instead), hence, generating more tokens with fewer characters.

There are other aspects of language and orthography one can ‘play’ with to analyse quantitatively, like whether words mainly end in a vowel or not, and which vowel mostly, and whether two successive vowels are acceptable for a language (for some, it isn’t). This is further described in the paper [3].

Yet, the UDHR is just one document. To examine the generalisability of these observations, we need to know whether the UDHR text is a ‘typical’ one. This was assessed in more detail by zooming in on isiZulu both quantitatively and qualitatively with four other corpora and texts in different genres. The results show that the UHDR is a typical text document orthographically, at least for the cumulative frequency distribution of the word length.

There were some other differences across the other corpora, which have to do with genre and datedness, which was observed elsewhere for whole words [4]. For instance, news items of isiZulu newspapers nowadays include words like iFacebook and EFF, which surely don’t occur in a century-old bible translation. They do violate the ‘no two successive vowels’ rule and the ‘final vowel’ rule, though.

On the qualitative side of the matter, and which will have an effect on searching for information in texts, text summarization, and error correction of spellcheckers, is, again, that agglutination. For instance, searching on imali ‘money’ alone would be woefully inadequate to find all relevant texts; e.g., those news items also include kwemali, yimali, onemali, osozimali, kwezimali, and ngezimali, which are, respectively of -, and -, that/which/who has -, of – (pl.), about/by/with/per – (pl.) money. Searching on the stem or root only is not going to help you much either, however. Take, for instance -fund-, of which the results of just two days of Isolezwe news articles is shown in the table below (articles from 2015, when there were protests, too). Depending on what comes before fund and what comes after it, it can have a different meaning, such as abafundi ‘students’ and azifundi ‘they do not learn’.

isolezwefund

Placing this is the broader NLP scope, it also affects the widely-used notion of lexical diversity, which, in its basic form, is a type-to-token ratio. Lexical diversity is used as a proxy measure for ‘difficulty’ or level of a text (the higher the more difficult), language development in humans as they grow up, second-language learning, and related topics. Letting that loose on isiZulu text, it will count abafundi, bafundi, and nabafundi as three different tokens, so wheehee, high lexical diversity, yet in English, it amounts to ‘students’, ‘students’ and ‘and the students’. Put differently, somehow we have to come up with a more meaningful notion of lexical diversity for agglutinating languages. A first attempt is made in the paper in its section 4 [3].

Thus, the last word has not been said yet about orthographic similarity, yet we now do have more insight into it. The surprising similarity of isiZulu (South Africa) with Runyankore (Uganda) was exploited in another research activity, and shown to be very amenable to bootstrapping [5], so, in its own way providing supporting evidence for bootstrapping potential that the figure above also indicated as promising.

As a final comment on the tooling side of things, I did use NLTK (Python). It worked well for basic analyses of text, but it (and similar NLP tools) will need considerable customization for the agglutinating languages.

 

References

[1] C. Meinhof. 1932. Introduction to the phonology of the Bantu languages . Dietrich Reiner/Ernst Vohsen, Johannesburg. Translated, revised and enlarged in collaboration with the author and Dr. Alice Werner by N.J. Van Warmelo.

[2] L. Pretorius and S. Bosch. Exploiting cross-linguistic similarities in Zulu and Xhosa computational morphology: Facing the challenge of a disjunctive orthography. In Proceedings of the EACL 2009 Workshop on Language Technologies for African Languages – AfLaT 2009, pages 96–103, 2009.

[3] C.M. Keet. An assessment of orthographic similarity measures for several African languages. Technical report, arxiv 1608.03065. August 2016.

[4] Ndaba, B., Suleman, H., Keet, C.M., Khumalo, L. The Effects of a Corpus on isiZulu Spellcheckers based on N-grams. IST-Africa 2016. May 11-13, 2016, Durban, South Africa.

[5] J. Byamugisha, C. M. Keet, and B. DeRenzi. Bootstrapping a Runyankore CNL from an isiZulu CNL. In B. Davis et al., editors, 5th Workshop on Controlled Natural Language (CNL’16), volume 9767 of LNAI, pages 25–36. Springer, 2016. 25-27 July 2016, Aberdeen, UK.