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.

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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.