December 14, 2018

Malayalam spellchecker – a morphology analyser based approach

Malayalam spellchecker – a morphology analyser based approach

A detailed note by Santhosh Thottingal.

My first attempt to develop a spellchecker for Malayalam was in 2007. I was using hunspell and a word list based approach. It was not successful because of rich  morphology of Malayalam. Even though I prepared a  manually curated 150K  words list, it was nowhere near to cover practically infinite words of  Malayalam. For languages with productive morphological processes in  compounding and derivation that are capable of generating dictionaries  of infinite length, a morphology analysis and generation system is  required. Since my efforts towards building such a morphology analyser  is progressing well, I am proposing a finite state transducer based  spellchecker for Malayalam. In this article, I will first analyse the  characteristics of Malayalam spelling mistakes and then explain how an  FST can be used to implement the solution.

What is a spellchecker?

The spellchecker is an application that tells whether the given word  is spelled correctly as per the language or not. If the word is not  spelled correctly, the spellchecker often gives possible alternatives as  suggestion to correct the misspelled word. The word can be spellchecked  independently or in the context of a sentence. For example, in the  sentence “അസ്തമയസൂര്യൻ കടലയിൽ മുങ്ങിത്താഴ്ന്നു”, the word “കടലയിൽ” is  spelled correctly if considered independently. But in the context of the  sentence, it is supposed to be “കടലിൽ”.

The correctness of the word is tested by checking if that word is in  the language model. The language model can be simply a list of all known  words in the language. Or it can be a system which knows how a word in a  language will look like and tell whether the given word is such a word.  In the case of Malayalam, we saw that the finite dictionary is not  possible. So we will need a system which is ‘aware’ of all words in the  language. We will see how a morphology analyser can be such a system.

If the word is misspelled, the system need to give correction. To  generate the correctly spelled words from a misspelled word form, an  error model is needed. The most common error model is Levenshtein edit distance.  In the edit distance algorithm, the misspelling is assumed to be a  finite number of operations applied to characters of a string: deletion, insertion, change, or transposition. The number of operations is known as ‘edit distance‘.  Any word from the known list of words in the language, with a minimal  distance is a candidate for suggestion. Peter Norvig explains such a  functional spellchecker in his article “How to Write a spelling corrector?

There are multiple problems with the edit distance based correction mechanism

  • For a query word, to generate all candidates after applying the  four operations, we can calculate the number of words we need to  generate and test its correctness. For a word of length n, an alphabet  size a, an edit distance d=1, there will be n deletions, n-1  transpositions, a*n alterations, and a*(n+1) insertions, for a total of 2n+2an+a-1  terms at search time. In the case of Malayalam, a is 117 if we consider  all encoded characters in Unicode version 11. If we remove all archaic  characters, we still need about 75 characters. So, for edit distance  d=1, a=75, for a word with 10 characters, 2*10+2*75*10+75-1 = 1594 and  much larger for larger d. So, you will need to do 1594  lookups(spellchecks) in the language model to get possible suggestions.
  • The  concept that the 4 edit operations are the cause for all spelling  mistakes is not accurate for Malayalam. There are many common spelling  mistakes in Malayalam that are 3 or 4 edit distance from the original  word. Usually the edit distance based corrections won’t go beyond d=2  since the number of candidates increases.

The problems with hunspell based spellchecker and Malayalam

Hunspell has a limited compounding support, but limited to two  levels. Malayalam can have more than 2 level compounding and sometimes  the agglutinated words is also inflected. Hunspell system has an affix dictionary and suffix mapping system. But it is very limited to support complex morphology like Malayalam.  With the help of Németh László, Hunspell developer, I had explored this  path. But abandoned due to many limitation of Hunspell and lack of  programmatic control of the morphological rules.

Nature of Malayalam spelling mistakes

Malayalam uses an alphasyllabary writing system. Each letter you  write corresponds to the grapheme representation of a phoneme. In  broader sense Malayalam can be considered as a language with one to one   grapheme to phoneme correspondence. Where as in English and similar  languages, letters might represent a variety of sounds, or the same  sounds can be written in different ways. The way a person learns writing  a language strongly depends on the writing system.

In Malayalam, since there is one and only one set of characters that  can correspond to a syllable, the confusion of letters does not happen.  For example, in English, Education, Ship, Machine, Mission all has sh sound [ʃ]. So a person can mix up these combinations. But in Malayalam, if it is sh sound [ʃ], then it is always ഷ.

Because of this, the spelling mistakes that is resulted by four edit  operations(deletion, insertion, change, or transposition) may not be an  accurate classification of errors in Malayalam.  Let us try to classify  and analyse the spelling mistake patterns of Malayalam.

  1. Phonetic approximation: The 1:1 grapheme to  phoneme correspondence is the theory. But because of this the inaccurate  utterance of syllables will cause incorrect spellings. For example,  ബൂമി is a relaxed way of reading for ഭൂമി since it is relatively  effortless. Since the relaxed way of pronunciation is normal, sometimes  people think that they are writing in wrong way and will try to correct  it unnecessarily പീഢനം->പീഡനം is one such example.Consonants:  Each consonant in Malayalam has aspirated, unaspirated, voiced and  unvoiced variants. Between them, it is very usual to get mixed upAspirated and Unaspirated mix-up:  Aspirated consonant can be mistakenly written as  Unaspirated  consonant. For Example, ധ -> ദ, ഢ -> ഡ . Similarly Unaspirated  consonant can be mistakenly written as aspirated consonant – Example, ദ  ->ധ, ഡ ->ഢ.Voiced and Voiceless mix-up. Voiced consonants like ഗ, ഘ can be mistakenly written as voiceless forms ക, ഖ. And vice versa.Gemination  of consonants is often relaxed or skipped in the speech, hence it  appear in writing too. Gemination in Malayalam script is by combining  two consonants using virama. നീലതാമര/നീലത്താമര is an example for this  kind of mistakes. There are a few debatable words too, like  സ്വർണം/സ്വർണ്ണം, പാർടി/പാർട്ടി. Another way of consonant stress  indication is by using Unaspirated Consonant + Virama + Aspirated Consonant. അദ്ധ്യാപകൻ/അധ്യാപകൻ, തീർഥം/തീർത്ഥം, വിഡ്ഡി/വിഡ്ഢി pairs are examples.Hard, Soft variants confusion. Examples: ശ/ഷ, ര/റ, ല/ളVowels: Vowel elongation or shortening, gliding vowels and semi vowels are the cause for vowel related mistakes in writing.Each  vowel in Malayalam can be a short vowel or long vowel. Local dialect  can confuse people to use one for the other. ചിലപ്പൊൾ/ചിലപ്പോൾ is one  example. Since many input tools place the short and long vowels forms  with very close keystrokes, it is possible to cause errors. In Inscript  keyboard, short and long vowels are in normal and shift position. In  transliteration based input methods, long vowel is often typed by  repeated keys(i, ii for ി, ീ). The vowel ഋ is close to റി  or റു in pronunciation. Example: ഋതു/റിതു. The vowel sign of ഋ while  appearing with a consonant is close to ്ര. Example ഗൃഹം/ഗ്രഹം.  ഹൃദയം/ഹ്രുദയം. Gliding vowels ഐ, ഔ get confused with its constituent vowels. കൈ/കഇ/കയ്, ഔ/അഉ/അവു are example.In  Malayalam, there is a tendency to use എ instead of ഇ, since the reduced  effort. Examples: ചിലവ്/ചെലവ്, ഇല/എല, തിരയുക/തെരയുക. Due to wide usage  of these variants, it is sometimes very difficult to say one word is  wrong. See the discussion about the ‘Standard Malayalam’ at the end of  this essay.Chillus: Chillus are pure  consonants. A consonant + virama sequence sometimes has no phonetic  difference from a chillu. For example, കല്പന/കൽപന, നിൽക്കുക/നില്ക്കുക  combinations. The chillu ർ is sometimes confused with ഋ sign. Examples  are: പ്രവർത്തി/പ്രവൃത്തി. The chillu form of മ – ം can appear are as  anuswara or ma+virama forms. Examples: പംപ, പമ്പ. But it is not rare to  see പംമ്പ for this. Sometimes, the anuswara get confused with ന്, and  പമ്പ becomes പന്പ. There were a few buggy fonts that used ന്+പ for മ്പ  ligature too.
  2. Weak Phoneme-Grapheme correspondence:  Due to historic or evolutionary nature of the script, Malayalam also  has some phonemes which has a weak relationship with the graphemes.ഹ്മ/  മ്മ as in ബ്രഹ്മം/ബ്രമ്മം, ന്ദ/ന്ന as in നന്ദി/നന്നി, ഹ്ന/ന്ന  as in  ചിഹ്നം/ചിന്നം are some examples where what you pronounce is not exactly  same as what you write.റ്റ, ന്റ – These two highly used  conjuncts heavily deviate from the letters and pronunciation. While  writing using pen, people don’t make much mistakes since they just draw  the shape of these ligatures, but while typing, one need to know the  exact key sequence and they get confused. Common mistakes for these  conjuncts are ററ, ൻറ, ൻറ്റ , ൻററ
  3. Visual similarity:  While using visual input methods such as handwriting based or some  onscreen keyboards, either the users or the input tool makes mistakes  due to visual similarityൃ, ്യ often get confused.ജ്ഞ, ഞ്ജ is one very common sequence where people are confused. ആദരാജ്ഞലി/ആദരാഞ്ജലി.ത്സ, ഝ is another combinationThe handwriting based input methods like Google handwriting tool is known for recognizing anuswara ം as zero, English o, O etc.When  people don’t know how to insert visarga ഃ, and since there is a very  similar key in keyboard- colon : they use it. Example: ദുഃഖം/ദു:ഖംള്ള,  the geminated form of ള, is very similar to two adjacent ള. This kind  of mistakes are very frequent among people whi studied Malayalam  inputting informally. Two adjacent റ, is another mistake for റ്റ,The  informal, trial-and-error based Malayalam inputting training also  introduced some other mistakes such as using open parenthesis ‘(‘ for  ്ര, closing parenthesis ‘)’ for ാ sign.
  4. Ambiguity due to regional dialect:  A good example for this is insertion of യ് in verbs.  കുറക്കുക/കുറയ്ക്കുക, ചിരിക്കുക/ചിരിയ്ക്കുക, Also in nominal inflections:  പൂച്ചയ്ക്ക്/പൂച്ചക്ക്.  Usuage of Samvruthokaram to distinguish between  a pure consonant and stressed consonant at the end of word is a highly  debated topic. For example, അവന്/അവനു്/അവനു. All these forms are common,  even though the usage of നു് is less after the script reformation. But  since script reformation was not an absolute transformation, it still  exist in usage
  5. Spaces: Malayalam is an  agglutinative language. Words can be agglutinated, but nothing prevents  people to put space and write in simple words. But this should be done  carefully since it can alter the meaning. An example is “ആന പുറത്തു  കയറി”, ആനപ്പുറത്തു കയറി”, “ആനപ്പുറത്തുകയറി”, “ആനപ്പുറത്ത് കയറി”. Another  example: “മലയാള ഭാഷ”, “മലയാളഭാഷ” – Here, there is no valid word  “മലയാള”. The anuswara at the end get deleted only when it joins with ഭാഷ  as adjective. A morphology analyser can correctly parse “മലയാളഭാഷ” as  മലയാളം<proper-noun><adjective>ഭാഷ<noun>. But since  language already broke this rule and many people are liberally using  space, a spellchecker would need to handle this cases.
  6. Slip of Finger:  Accidental insertions or omissions of key presses is the common reason  for spelling mistakes. For alphabetic language, mostly this type of  errors are addressed. For Malayalam also, this type of accidental slip  of finger can happen. For Latin based languages,  we can make some  analysis since we know a QWERTY keyboard layout and do optimized checks  for this kind of issues. Since Malayalam will use another level of  mapping on top of QWERTY for inputting(inscript, phonetic,  transliteration), it is not easy to analyse this errors. So, in general,  we can expect random characters or omission of some characters in the  query word. An accidental space insertion has the challenge that it will  split the word to two words and if the spellchecking is done by one  word at a time, we will miss it.

I must add that the above classification is not based on a systematic  study of any test data that I can share. Ideally, this classification  should done with real sample of Malayalam written on paper and computer.  It should be then manually checked for spelling mistakes, list down the  mistakes and analyse the patterns. This exercise would be very  beneficial for spellcheck research. In my case, even since I released my  word list based spellchecker, noticing spelling errors in  internet(social media, mainly) has been my obsession. Sometimes I also  tried to point out spelling mistakes to authors and that did not give  much pleasant experience to me. The above list is based on my observation from such patterns.

Malayalam spelling checker

To check if a word is valid, known, correctly spelled word, a simple  look up using morphology analyser is enough. If the morphology analyser  can parse the word, it is correctly spelled. Note that the word can be  an agglutinated at arbitrary levels and inflected at same time.

Out of lexicon words

Compared to the finite set word list, the FST based morphology  analyser and generator system covers large number of words using its  generation system based on morpho-phonotactics. For a discussion on this  see my previous blog post about the coverage test. Since every language vocabulary is a dynamic system, it is still  impossible to cover 100% words in a language all the time. New words get  added to language every now and then. There are nouns related to  places, people names, product names etc that is not in the lexicon of  Morphology analyser. So, these words will be reported as unknown words  by the spellchecker. Unknown word is interpreted as misspelled word too.  This issue is a known problem. But since a spellchecker is often used  by a human user, the severity of the issue depends whether the  spellchecker does not know about lot of commonly used words or not. Most  of the spellcheckers provide an option to add to dictionary to avoid  this issue.

As part of the Morphology analyser, the expansion of the lexicon is a  never ending task. As the lexicon grows, the spellchecker improves  automatically.

Malayalam spelling correction

To provide spelling suggestions, the FST based morphology analyser can be used. This is a three step process

  1. Generate a list of candidate words from the query word. The  words in this list may be incorrect too. The words are generated based  on the patterns we defined based on the nature of spelling mistakes. We  scan the query word for common patterns of errors and apply fix for that  pattern. Since there dozens of patterns, we will have many candidate  words.
  2. From the candidate list, find out the correctly spelled  word using spellcheck method. This will result a very small number of  words. These words are the probable replacements for the misspelled  query word.
  3. Sort the candidate words to provide more  probable suggestion as the first one. For this, we can do a ranking on  the suggestion strategies. A very common error pattern get high priority  at step 1. So the suggestions from that appear first in the candidate  list. A more sophisticated approach would use a frequency model for the  words. So candidate words that are very frequent in the language will  appear as first candidate.

One thing I observed from the above approach is, in reality the  candidate words after all the above steps for Malayalam is most of the  time one or two. This make step 3 less relevant. At the same time, an  edit distance based approach would have generated more than 5 candidate  words for each misspelled word. The candidates from the edit distance  based suggestion mechanism would be very diverse, meaning, they won’t  have be related to the indented word at all.  The following images  illustrates the difference.

spelling suggestion from the morphology analyser based system.
Spelling suggestions from edit distance based candidates

Context sensitive spellchecking

Usually the spellchecking and suggestion are done at one word at a  time. But if we know the context of the word, the spellchecking will be  further useful. The context is usually the words before and after the  word. An example from English is “I am in Engineer”. Here the word “in”  is a correct word, but with in the context, it is wrong. To mark the  word “in” wrong, and provide ‘an’ as suggestion, one approach is ngram  model of part of speech for the language. In simple words, what kind of  word can appear in between a known kind of words. If we build this model  for a language, that will surely tell that the a locative POS “in”  before Engineer is rare or not seen before.

The Standard Malayalam or lack thereof

How do you determine which is the “correct” or “standard” way of  writing a word? Malayalam has lot of orthographic variants for words  which were introduced to language as genuine mistakes that later became  common words(രാപ്പകൽ/രാപകൽ, ചിലവ്/ചെലവ്), phonetic  simplification(അദ്ധ്യാപകൻ/അധ്യാപകൻ, സ്വർണ്ണം/സ്വർണം), or old  spelling(കർത്താവ്/കൎത്താവു്) and so on. A debate about the correctness  of these words will hardly reach conclusion. For our case, this is more  of an issue of selecting words in the lexicon. Which one to include,  which one to exclude? It is easy to consider these debates as blocker  for the progress of the project and give up: “well, these things are not  decided by academics so far, so we cannot do anything about it till  they make up their mind”.

I did not want to end up in that deadlock. I decided to be liberal  about the lexicon. If people are using some words commonly, they are  valid words the project need to recognize as much as possible. That is  the very liberal definition I have. I leave the standardization  discussion to linguists who care about it.

The news report from Mathrubhumi daily in 2007 about my old spelling checker

Back in 2007, when I developed the old Malayalam spellchecker, these  debates came up.  Dr. P Somanathan, who helps me a lot now a days with  this project, wrote about the issue of Malayalam spelling  inconsistencies: “ചരിത്രത്തെ വീണ്ടെടുക്കുക:” and “വേണം നമുക്ക് ഏകീകൃതമായ ഒരെഴുത്തുരീതി“.


  1. A Data-Driven Approach to Checking and Correcting Spelling Errors in Sinhala. Asanka Wasala, Ruvan Weerasinghe, Randil Pushpananda,Chamila Liyanage and Eranga Jayalatharachchi [pdf] This paper discuss the phonetic similarity based strategies to create a wordlist, instead of edit distance approach.
  2. Finite-State Spell-Checking with Weighted Language and Error Models—Building and Evaluating Spell-Checkers with Wikipedia as Corpus Tommi A Pirinen, Krister Lindén [pdf]  This paper outlines the usage of Finite state transducer technique to  address the issue of infinite dictionary of morphologically rich  languages. They use Finnish as the example language
  3. The Malayalam morphology analyser project by myself is the foundation for the spellchecker.
  4. The common Malayalam spelling mistakes and confusables were presented in great depth by Renowned linguist and author Panmana Ramachandran Nair in his books  ‘തെറ്റില്ലാത്ത മലയാളം’, ‘തെറ്റും ശരിയും’, ‘ശുദ്ധ മലയാളം’ and ‘നല്ല മലയാളം’.
  5. Improving Finite-State Spell-Checker Suggestions with Part of Speech N-Grams Tommi A Pirinen and Miikka Silfverberg and Krister Lindén [pdf] – This paper discuss the context sensitive spellchecker approach.

Where can I try the spellchecker?

A screenshot of Malayalam spellchecker in action. Along with incorrect words, some correct words are marked as misspelled too. This is because of the incomplete morphology analyser. As it improves, more words will be covered.

If you curious about the implementation of this approach, please refer and Since the implementation is not complete, I will write a new article about it later. Thanks for reading!

This was originally written by Santhosh Thottingal and published at