# 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.

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

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:

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.

processing one error at a time

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.

The 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])

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.

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.

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]).

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’.

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.

# On generating isiZulu sentences with part-whole relations

It all sounded so easy… We have a pretty good and stable idea about part-whole relations and their properties (see, e.g., [1]), we know how to ‘verbalise’/generate a natural language sentence from basic description logic axioms with object properties that use simple verbs [2], like $Professor \sqsubseteq \exists teaches.Course$ ‘each professor teaches at least one course’, and SNOMED CT is full of logically ‘simple’ axioms (it’s in OWL 2 EL, after all) and has lots of part-whole relations. So why not combine that? We did, but it took some more time than initially anticipated. The outcomes are described in the paper “On the verbalization patterns of part-whole relations in isiZulu”, which was recently accepted at the 9th International Natural Language Generation Conference (INLG’16) that will be held 6-8 September in Edinburgh, Scotland.

What it ended up to be, is that notions of ‘part’ in isiZulu are at times less precise and other times more precise compared to the taxonomy of part-whole relations. This interfered with devising the sentence generation patterns, it pushed the number of ‘elements’ to deal with in the language up to 13 constituents, and there was no way to avoid proper phonological conditioning. We already could handle quantitative, relative, and subject concords, the copulative, and conjunction, but what had to be added were, in particular, the possessive concord, locative affixes, a preposition (just the nga in this context), epenthetic, and the passive tense (with modified final vowel). As practically every element has to be ‘completed’ based on the context (notably the noun class), one can’t really speak of a template-based approach anymore, but a bunch of patterns and partial grammar engine instead. For instance, plain parthood, structural parthood, involvement, membership all have:

• (‘each whole has some part’) $QCall_{nc_{x,pl}}$ $W_{nc_{x,pl}}$ $SC_{nc_{x,pl}}-CONJ-P_{nc_y}$ $RC_{nc_y}-QC_{nc_y}-$dwa
• (‘each part is part of some whole’) $QCall_{nc_{x,pl}}$ $P_{nc_{x,pl}}$ $SC_{nc_{x,pl}}-COP-$ingxenye $PC_{\mbox{\em ingxenye}}-W_{nc_y}$ $RC_{nc_y}-QC _{nc_y}-$dwa

There are a couple of noteworthy things here. First, the whole-part relation does not have one single string, like a ‘has part’ in English, but it is composed of the subject concord (SC) for the noun class (nc) of the noun that play the role of the whole ( W ) together with the phonologically conditioned conjunction na- ‘and’ (the “SC-CONJ”, above) and glued onto the noun of the entity that play the role of the part (P). Thus, the surface realisation of what is conceptually ‘has part’ is dependent on both the noun class of the whole (as the SC is) and on the first letter of the name of the part (e.g., na-+i-=ne-). The ‘is part of’ reading direction is made up of ingxenye ‘part’, which is a noun that is preceded with the copula (COP) y– and together then amounts to ‘is part’. The ‘of’ of the ‘is part of’ is handled by the possessive concord (PC) of ingxenye, and with ingxenye being in noun class 9, the PC is ya-. This ya- is then made into one word together with the noun for the object that plays the role of the whole, taking into account vowel coalescence (e.g., ya-+u-=yo-). Let’s illustrate this with heart (inhliziyo, nc9) standing in a part-whole relation to human (umuntu, NC1), with the ‘has part’ and ‘is part of’ underlined:

• bonke abantu banenhliziyo eyodwa ‘All humans have as part at least one heart’
• The algorithm, in short, to get this sentence from, say $Human \sqsubseteq \exists hasPart.Heart$: 1) it looks up the noun class of umuntu (nc1); 2) it pluralises umuntu into abantu (nc2); 3) it looks up the quantitative concord for universal quantification (QCall) for nc2 (bonke); 4) it looks up the SC for nc2 (ba); 5) then it uses the phonological conditioning rules to add na- to the part inhliziyo, resulting in nenhliziyo and strings it together with the subject concord to banenhliziyo; 6) and finally it looks up the noun class of inhliziyo, which is nc9, and from that it looks up the relative concord (RC) for nc9 (e-) and the quantitative concord for existential quantification (QC) for nc9 (being yo-), and strings it together with –dwa to eyodwa.
• zonke izinhliziyo ziyingxenye yomuntu oyedwa ‘All hearts are part of at least one human’
• The algorithm, in short, to get this sentence from $Heart \sqsubseteq \exists isPartOf.Human$: 1) it looks up the noun class of inhliziyo (nc9); 2) it pluralises inhliziyo to izinhliziyo (nc10); 3) it looks up the QCall for nc10 (zonke); 4) it looks up the SC for nc10 (zi-), takes y- (the COP) and adds them to ingxenye to form ziyingxenye; 5) then it uses the phonological conditioning rules to add ya- to the whole umuntu, resulting in yomuntu; 6) and finally it looks up the noun class of umuntu, which is nc1, and from that the RC for nc10 (o-) and the QC for nc10 (being ye-), and strings it together with –dwa to oyedwa.

For subquantities, we end up with three variants: one for stuff-parts (as in ‘urine has part water’, still with ingxenye for ‘part’), one for portions of solid objects (as in ‘tissue sample is a subquantity of tissue’ or a slice of the cake) that uses umunxa instead of ingxenye, and one ‘spatial’ notion of portion, like that an operating theatre is a portion of a hospital, or the area of the kitchen where the kitchen utensils are is a portion of the kitchen, which uses isiqephu instead of ingxenye. Umunxa is in nc3, so the PC is wa- so that with, e.g., isbhedlela ‘hospital’ it becomes wesibhedlela ‘of the hospital’, and the COP is ng- instead of y-, because umunxa starts with an u. And yet again part-whole relations use locatives (like the containment type of part-whole relation). The paper has all those sentence generation patterns, examples for each, and explanations for them.

The meronymic part-whole relations participation and constitution have added aspects for the verb, such as generating the passive for ‘constituted of’: –akha is ‘to build’ for objects that are made/constituted of some matter in some structural sense, else –enza is used. They are both ‘irregular’ in the sense that it is uncommon that a verb stem starts with a vowel, so this means additional vowel processing (called hiatus resolution in this case) to put the SC together with the verb stem. Then, for instance za+akhiwe=zakhiwe but u+akhiwe=yakhiwe (see rules in paper).

Finally, this was not just a theoretical exercise, but it also has been implemented. I’ll readily admit that the Python code isn’t beautiful and can do with some refactoring, but it does the job. We gave it 42 test cases, of which 38 were answered correctly; the remaining errors were due to an ‘incomplete’ (and unresolvable case for any?) pluraliser and that we don’t know how to systematically encode when to pick akha and when enza, for that requires some more semantics of the nouns. Here is a screenshot with some examples:

The ‘wp’ ones are that a whole has some part, and the ‘pw’ ones that the part is part of the whole and, in terms of the type of axiom that each function verbalises, they are of the so-called ‘all some’ pattern.

The source code, additional files, and the (slightly annotated) test sentences are available from the GENI project’s website. If you want to test it with other nouns, please check whether the noun is already in nncPairs.txt; if not, you can add it, and then invoke the function again. (This remains this ‘clumsily’ until we make a softcopy of all isiZulu nouns with their noun classes. Without the noun class explicitly given, the automatic detection of the noun class is not, and cannot be, more than about 50%, but with noun class information, we can get it up to 90-100% correct in the pluralisation step of the sentence generation [4].)

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. Basics for a grammar engine to verbalize logical theories in isiZulu. 8th International Web Rule Symposium (RuleML’14), A. Bikakis et al. (Eds.). Springer LNCS vol. 8620, 216-225. August 18-20, 2014, Prague, Czech Republic.

[3] 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. (in print)

[4] 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. (in print)

# Bootstrapping a Runyankore CNL from an isiZulu one mostly works well

Earlier this week the 5th Workshop on Controlled Natural Language (CNL’16) was held in Aberdeen, Scotland, where I presented progress made on a Runyankore CNL [1], rather than my student, Joan Byamugisha, who did most of the work on it (she could not attend due to nasty immigration rules by the UK, not a funding issue).

“Runyankore?”, you might ask. It is one of the languages spoken in Uganda. As Runyankore is very under-resourced, any bootstrapping to take a ‘shortcut’ to develop language resources would be welcome. We have a CNL for isiZulu [2], but that is spoken in South Africa, which is a few thousand kilometres further south of Uganda, and it is in a different Guthrie zone of the—in linguistics still called—Bantu languages, so it was a bit of a gamble to see whether those results could be repurposed for Runynakore. They could, needing only minor changes.

What stayed the same were the variables, or: components to make up a grammatically correct sentence when generating a sentence within the context of OWL axioms (ALC, to be more precise). They are: the noun class of the name of the concept (each noun is assigned a noun class—there are 20 in Runyankore), the category of the concept (e.g., noun, adjective), whether the concept is atomic (named OWL class) or an OWL class expression, the quantifier used in the axiom, and the position of the concept in the axiom. The only two real differences were that for universal quantification the word for the quantifier is the same when in the singular (cf. isiZulu, where it changes for both singular or plural), and for disjointness there is only one word, ti ‘is not’ (cf. isiZulu’s negative subject concord + pronomial). Two very minor differences are that for existential quantification ‘at least one’, the ‘at least’ is in a different place in the sentence but the ‘one’ behaves exactly the same, and ‘all’ for universal quantification comes after the head noun rather than before (but is also still dependent on the noun class).

It goes without saying that the vocabulary is different, but that is a minor aspect compared to figuring out the surface realisation for an axiom. Where the bootstrapping thus came in handy was that that arduous step of investigating from scratch the natural language grammar involved in verbalising OWL axioms could be skipped and instead the ones for isiZulu could be reused. Yay. This makes it look very promising to port to other languages in the Bantu language family. (yes, I know, “one swallow does not a summer make” [some Dutch proverb], but one surely is justified to turn up one’s hope a notch regarding generalizability and transferability of results.)

Joan also conducted a user survey to ascertain which surface realisation was preferred among Runyankore speakers, implemented the algorithms, and devised a new one for the ‘hasX’ naming scheme of OWL object properties (like hasSymptom and hasChild). All these details, as well as the details of the Runyankore CNL and the bootstrapping, are described in the paper [1].

I cannot resist a final comment on all this. There are people who like to pull it down and trivialise natural language interfaces for African languages, on the grounds of “who cares about text in those kind of countries; we have to accommodate the illiteracy with pictures and icons and speech and such”. People are not as illiterate as is claimed here and there (including by still mentally colonised people from African countries)—if they were, then the likes of Google and Facebook and Microsoft would not invest in localising their interfaces in African languages. The term “illiterate” is used by those people to include also those who do not read/write in English (typically an/the official language of government), even though they can read and write in their local language. People who can read and write—whichever natural language it may be—are not illiterate, neither here in Africa nor anywhere else. English is not the yardstick of (il)literacy, and anyone who thinks it is should think again and reflect a bit on cultural imperialism for starters.

References

[1] Byamugisha, J., Keet, C.M., DeRenzi, B. Bootstrapping a Runyankore CNL from an isiZulu CNL. 5th Workshop on Controlled Natural Language (CNL’16), Springer LNAI vol. 9767, 25-36. 25-27 July 2016, Aberdeen, UK. Springer’s version

[2] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2016. DOI: 10.1007/s10579-016-9340-0 (in print) accepted version

# Preliminary promising results on a data-driven spellchecker for isiZulu

While developing a spellchecker for isiZulu is not new, the one for Open Office v3 doesn’t work with the more up-to-date versions of Open Office, it was of limited quality, the other papers on isiZulu spellcheckers have no tools for it, and the techniques used were tailored to the language. The demand for it has not decreased, however. Last year’s honours student I co-supervised, Balone Ndaba, set out to address this using n-grams and several corpora, so if this were to work, then this essentially language-independent solution may be ported to other Bantu languages. The data-driven approach worked reasonably well (up to 89% accuracy), so it all ended up as a paper entitled “The effects of a corpus on isiZulu spellcheckers based on n-grams” [1], co-authored with Balone Ndaba, Hussein Suleman, and Langa Khumalo. It has been accepted at the IST Africa’16 conference that will take place in less than two weeks in Durban, South Africa. The material can be downloaded from Balone’s honours project page.

In a nutshell, well less than one page cf. the paper’s 9 pages, the paper describes the design, implementation, and experimental results of a statistics-based spellchecker. One option in a data-driven approach is to make long wordlists to look up the word to be spellchecked, indeed, but this is not doable due to the agglutination in the language (i.e., way too many options), and there are limited curated resources anyhow. Instead, we let it ‘learn’ using a statistical language model using n-grams created from a corpus. For instance, take the word yebo ‘yes’, then its bigrams are ye, eb, and bo, its trigrams are yeb and ebo, and quadrigram (i.e., 4 characters) yebo. Feeding the algorithm a lot of text generates a lot of n-grams, some of which occur much more often than others, whereas the very rare ones were probably typos or a loan word in the original text. The hope is then that when it is fed a word that it has not been trained on, it can recognize that it is (very probably) still a valid word in the language, as the sequence of the characters in the string are deemed common enough, or, conversely, that it is very likely to be misspelled.

It was not clear upfront which of the variables will affect the quality most, and which combination leads to the best spellchecker performance. This could be the threshold for when to include the n-gram, how big the n-gram should be, and whether the training corpus had any effect. So, all this was tested (see paper for details).

Trigrams worked better than quadrigrams, and a threshold of 0.003 worked better than the other thresholds. This is the ‘easy’ part, in a way. What complicated matters were the corpora, where one was much better to learn from or test on than the other. This is summarised in the table below. UC stands for the Ukwabelana corpus [2] that consists of the bible and a few copyright-expired novels, S. INC for a sample of the isiZulu National Corpus that is being developed at the University of KwaZulu-Natal [3], and INC is a small corpus of news items from the Isolezwe and news24 in isiZulu news websites (extended from the earlier playing with some of those articles). The INC and NIC work well on each other, but not at all with UC, and if trained with UC then only mediocre on the more recent texts of NIC and INC. So, the training corpus has a rather large effect on the spellchecker’s performance.

Lexical recall with n-grams: 10-fold cross-validation (i.e., with itself) and tested against the other corpora (t. = test).

 10-fold Train UC Train NIC Train S. INC 3-gr. 4-gr. t. NIC t. INC t. INC t. UC t. UC t. NIC UC 85 80 30 41 S. INC 66 63 54 89 NIC 86 79 70 89

We did look into the corpora a little more, but there’s more to analyse. Notably, the number of unique words in the corpus seems to matter more than its size, and the datedness and quality of the text suggest room for additional evaluation. UC contains some archaic terms that wouldn’t be used in modern-day isiZulu, and there have been some errors introduced in the words, assumed to be due to OCR (words with three successive vowels are a no-no in isiZulu, verbs missing the final vowel).

Overall, though, a lexical recall and an accuracy of 89% is quite alright for a first-pass, meriting resources for fine-tuning for isiZulu and it being promising as approach for related languages.

Balone will present the paper at IST Africa, and Hussein and Langa will also participate, so you can ask them more details in person at the conference. (I’ll be holding a final training for the team representing UCT in the ACM ICPC World Finals and pack my bags to join them as coach in Phuket in Thailand.)

References

[1] 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.

[2] S. Spiegler, A. van der Spuy, P. Flach. Ukwabelana – An Open-source Morphological Zulu Corpus. Proceedings of the 23rd International Conference on Computational Linguistics (COLING, 2010). ACL. 1020-1028.

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