NANYANG TECHNOLOGICAL UNIVERSITYSCHOOL OF HUMANITIES AND SOCIAL SCIENCES

Creating derivational morphology links in
Wordnet Bahasa
Muhammad Zulhelmy bin Mohd Rosman
Matriculation No.: U1030148D
Supervisor: Associate Professor Francis Bond

Date:18 November 2013

A Final Year Project submitted to the School of Humanities and Social Sciences, Nanyang Technological
University in partial fulfilment of the requirements of the Degree of Bachelor of Arts in Linguistics andMultilingual Studies.

Declaration of AuthorshipI declare that this assignment is my own original work, unless otherwise
referenced, as defined by the NTU policy on plagiarism. I have read the NTU
Honour Code and Pledge.
No part of this Final Year Project has been or is being concurrently submitted forany other qualification at any other university.
I certify that the data collected for this project is authentic. I fully understand thatfalsification of data will result in the failure of the project and/or failure of the course.

______________________________________

Name

Signature

DateAcknowledgements
I would like to express my gratitude and appreciation to my supervisor Associate Professor Francis
Bond for his guidance and support throughout my Final Year Project and my University life as a whole. It
is through him that I was introduced to the wonderful and intriguing world of Computational Linguistics.
It is truly impressive that a non-native speaker would be interested in creating and developing the Wordnet Bahasa which in the long run will help the preservation of the Malay and Indonesian languages. Fromthe start of this project, his inputs allowed me to develop this project well and enabled me to understandthe subjects of morphology and word sense disambiguation better.

I would like to thank my friends and fellow course mates for their support throughout this period.
Even though their input were not required for this project, their encouragement allowed me to pushthrough the tough times.

iContents
1

Introduction

1

2

Derivational Morphology in Bahasa Melayu

2

2.1

Affixes in Bahasa Melayu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

2.1.1

Prefixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

2.1.2

Suffixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

2.1.3

Circumfixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

2.1.4

Infixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

2.1.5

Transparency of Root Word to Derived Forms . . . . . . . . . . . . . . . . . . .

7

3

4

5

Resources7

3.1

Current state of Wordnet Bahasa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

3.1.1

Derivational Morphology in Other Wordnets . . . . . . . . . . . . . . . . . . .

8

3.1.2

MorphInd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9Methodology

10

4.1

Analysis of Wordnet Bahasa using MorphInd . . . . . . . . . . . . . . . . . . . . . . .

11

4.2

Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

4.2.1

Using Wordnet Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

4.2.2

POS Based Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

4.2.3

Semantic Super Type Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

4.2.4

Using Wordnet Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

Results17

5.1

MorphInd Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

5.1.1

Derivational Link From Princeton Wordnet . . . . . . . . . . . . . . . . . . . .

18

POS and Semantic Super Type Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18

5.2.1

19

5.2

6

Using Wordnet Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Discussion

27

6.1

Evaluation of POS and Super Type Filter . . . . . . . . . . . . . . . . . . . . . . . . . .

27

6.2

Evaluation of WSD using Lesk algorithm . . . . . . . . . . . . . . . . . . . . . . . . .

28

ii
7

Further Work30

8Conclusion

32

Appendices35

Appendix A Release35

Appendix B Evaluation of MorphInd

37

Appendix C MorphInd Process by Affixes

38

iii
Abstract
Derivational morphology links are created for the Wordnet Bahasa, a combined Indonesian and
Malay online lexical dictionary (Nurril Hirfana, Suerya, & Bond, 2011). The focus was to link rootwords to affixed words as affixation is one of the more apparent word formation processes in Bahasa
Melayu. MorphInd, an Indonesian morphological analyser (Larasati, Kubon, & Zeman, 2011), isused to breakdown affixed words into their root form and affixes. Using Python 2.7 with NLTK, a
raw mapping is done by matching the analysed words to the root forms. The derivational links in the
Princeton Wordnet (PWN) are used to verify if the same links exist in Wordnet Bahasa. Redundant
links are removed by the Part-of-Speech (POS) filter and Semantic Super Type filter. The links arethen disambiguated using the Lesk algorithm, where the definitions and other components of thesense (e.g. hypernyms, hyponyms and examples) are compared for their similarity. However, thedisambiguation process is rendered ineffective because of the high amount of errors still existing in
Wordnet Bahasa. The derivational links are released as a separate file and only those with similar
derivational links to PWN are added into Wordnet Bahasa. Erroneous entries that were identified
using MorphInd are removed from Wordnet Bahasa.

11Introduction
Word Sense Disambiguation (WSD) is a process of identifying the most appropriate sense for words

with multiple meanings. Words are usually ambiguous in nature (Lesk, 1986; Baldwin et al., 2010). For
example, the English word bank has multiple meanings. It can either be “a sloping land” or “a financial
institution”, depending on the context that the word is in. With a more robust WSD process, the mostappropriate meaning can be tagged to the correct lexeme. However, having a standard framework for thisprocess is difficult as different languages have different morphological and syntactical structure (Bosch,Fellbaum, & Pala, 2008). Thus, different methods are usually applied to comply with the uniqueness ofeach language. Adding morpho-semantic relations into a computer database such as an online dictionary
is not an easy task. As some links are ambiguous, creating a fully automated program would not accurately reflect the links as in natural language (Bilgin, Cetinoglu, & Oflazer, 2004; Ranaivo-Malancon,2004). These linkages between words would be easily understandable to humans through usage and experience. However, the computer does not have this ability and therefore, a WSD process needs to bedone to ensure that a precise representation of the natural language will be shown (Ranaivo-Malancon,2004).
This study aims to create one of the morpho-semantic relations, the derivational morphology links,
in Wordnet Bahasa - a Wordnet created based on the Princeton Wordnet for the Malay and Indonesianlanguages (Nurril Hirfana, Suerya, & Bond, 2011). As the derivational links will encompass the whole
Wordnet Bahasa, WSD process is needed to identify the validity of the links. In general, wordnets do notinclude derivational links as part of it basic set up as morphologically derived words are assumed to be
regular. Therefore, affixes are also assumed to hold distinct information about the derived word’s meaningand part of speech (Fellbaum, Osherson, & Clark, 2009). However, as affixation in Bahasa Melayu is
not regular, the derivational links must be created to show the construction of the derived words and theiraffixes. With the derivational morphology links added into Wordnet Bahasa, a more representative of
the Bahasa Melayu structure will be shown. This study will only focus on the single word sense that isavailable in Wordnet Bahasa. As this is the first instance of creating derivational link in Wordnet Bahasa,
it would be best to investigate the accuracy of the single words links before embarking into multi words.
The following section will describe the types of derivational morphology that exist in Bahasa Melayu(S 2). The current state of Wordnet Bahasa will be discussed followed by the description of MorphInd
and how can it be useful in the creation of the derivational link (S 3). The methodology of the creationand disambiguation of the derivational links is thoroughly explained (S 4). The results of the mapping

2
are presented (S 5) and discussed (S 6).

2

Derivational Morphology in Bahasa Melayu
The Malay Language (Bahasa Melayu) is a part of the Austronesian language family and is mostly

spoken in the Malay Archipelago region. This consists of Indonesia, Malaysia, Brunei and Singapore
where it is an official language in all of these countries. Bahasa Melayu is also an unofficial languagein Thailand and Christmas Island. Currently, the total number of speakers stands at 163 million people
comprising of 23 million native speakers and 140 million L2 speakers.1 However, Kozok (2012) statedthat because Malay and Indonesian are institutionalised as national languages after independence, thetotal number of speakers should be closer to 215 million. Bahasa Melayu has two writing systems, theLatin script and Arabic (known as Jawi). From the 17th century onwards, the Latin script is much more
prominently used through the influence of the Dutch and British.2
Bahasa Melayu is an agglutinative language where new words are created by the manipulation of rootwords (Sneddon, 2010). This is done by three main methods: affixation, compounding and reduplication.
Affixation is done by attaching affixes onto a root word. As compared to root words, affixes do not havestandalone meaning. For example, the derived form berbatas ”having a boundary” is created by adding
the affix ber- to the root form batas “limit". Compounding is achieved by joining two root forms to create
a new word with a new meaning. For example, the word kakaktua "parrot" is from two root forms kakak"sister" and tua "old". Reduplication on the other hand is a repetition of the root word. The root word
tiba "arrive" can be reduplicated to form tiba-tiba "suddenly". In this study, the focus of the derived
morphology linkage is affixation as it is one of the more apparent word formation in Bahasa Melayu(Macdonald et al., 1976; Sneddon, 2010).

2.1

Affixes in Bahasa Melayu

Bahasa Melayu has all the four forms of affixes; prefixes, suffixes, infixes and circumfixes. Thissection will elaborate on the four different forms and how they are used to produce a new word. Most affixations in Bahasa Melayu are productive and create either derivational or inflectional forms (Macdonald
et al., 1976; Sneddon, 2010).
1 http://archive.ethnologue.com/16/show_language.asp?code=ind
2 http://www.omniglot.com/writing/malay.htm

3
Prefix

POS→DPOS

Root word

Meaning

Derived Form

Derived Meaning

ber-

N→V

anak

child

ber-anakgiving birth

per-

N→N

tiga

three

per-tiga

third

peN-

V→A

malas

lazy

pe-malaslazy bones

ter-

V→A

tarik

pull

ter-tarik

attracted

meN-

N→V

tari

a danceme-narito dance

di-

N→V

kapur

chalk

di-kapurto whitewash

ke-

V→N

hendakwant

ke-hendakdesire

se-

V→R

lalu

pass

se-lalu

alwaysTable 1: Examples of Prefixes in Bahasa Melayu with Derived Forms
2.1.1

Prefixes

Examples of the prefixes in Malay are shown in Table 1. Different prefixes in Bahasa Melayu havedifferent functions. For example, the prefix ber- derives verbs from noun roots while the prefix ke- derived
nouns from verb roots (Macdonald et al., 1976; Sneddon, 2010). Table 1 shows examples of how eachprefix creates a new derived form of the root word. the prefixes ber-, per- and ter- only create derivational
forms while the rest of the prefixes create both derivational and inflectional forms (Macdonald et al.,1976). There are also borrowed prefixes from other languages; such as anti-, mikro-, ultra- and superfrom English (Warrillow-Williams, 2002), and pra- from Sanskrit (Sneddon, 2010). Affixes can also bederived from another affixed form such as the prefix peN- where the noun form is derived from the root
word with prefix meN- (Macdonald et al., 1976) as seen in Table 2.
The allomorphic forms prefix meN- and peN- correspond to the phonetic environment of the stem.
For example, if the stem begins with a bilabial stop (eg /b/, /p/) then the allomorphic form mem- will be
used (Macdonald et al., 1976; Sneddon, 2010). These allomorphs appears in complementary distribution(Sneddon, 2010). For meN- and peN-, the allomorphic forms does assimilate to the root word. A single
affix meN- has various forms; men-, mem-, meng-, me-. While the prefix peN- has the allomorphic forms;
pen-, pem-, peng- and pe-. The allomorphs of the prefix meN- are shown in Table 3.
A combination of two prefixes can occur in Bahasa Melayu (Ranaivo-Malancon, 2004; Nakov &Ng, 2011). The prefix meN- and per- can be combine with the root word buat “create” to produce theword memperbuat “commit”. The prefix per- derives an instrument or agent noun while the prefix meN-

4
Prefix

POS→DPOS

Root word

Meaning

Derived Form

Derived Meaning

peN-

V→N

me-nuliswrite

pe-nulis

writerpeN-

V→Nmeng-gosok

rub

peng-gosok

polisherpeN- -an V→Nme-luas-kan

to widenpe-luas-an

broadening

peN- -an V→Nmeng-irim-kan send

peng-irim-an

delivery

Table 2: peN- Derived from Root Words with meNAllomorphic form

POS→DPOS

Root word

Meaning

Derived Form

Derived Meaning

me-

V→V

lihat

see

me-lihatseeing

men-

V→V

cari

find

men-cari

search

mem-

V→V

buka

open

mem-buka

to open

meng-

V→N

gambarpicturemeng-gambar

to describe

Table 3: Allomorphs of the Prefix meNindicates an active voice, However, when they are combined, the meaning of the combined prefix is notequal to the sum of the two previous prefixes. Unlike their singular forms, the prefix memper- derives
a transitive verb which is also causative (Macdonald et al., 1976). As the pattern in Bahasa Melayu isregular, the double prefix can be analysed as one unit (Ranaivo-Malancon, 2004; Sneddon, 2010) . This
is to ensure that they remain separate from the individual prefix form. Examples of the double prefixes
are in Table 4.
Double Prefix

POS→DPOS

Root word

Meaning

Derived Form

Derived Meaning

mem-per-

N→V

kaya

rich

meN-per-kaya

to enrichdi-per-N→Vsembahhomagedi-per-sembah

to pay homage

ber-per-N→Vlembagaestablishment ber-per-lembaga-an having constitution

ber-ke-

N→V

liar

wild

ber-ke-liar-an

Table 4: Double Prefixes in Bahasa Melayu

to stray5
SuffixRoot word

POS→DPOS

Meaning

Derived Form

Derived Meaning

-an

kotor

A→N

dirty

kotor-an

dirt

-kan

hamil

N→V

pregnant

hamil-kan

to impregnate

-i

luka

N→V

bruiseluka-ito hurt-nya

akibatN→R

resultakibat-nya

consequently

-anda

ibu

N→N

mother

ibu-anda

dear mother

-man

seni

N→N

art

seni-manartist

-wan

usaha

N→N

effortusaha-wanentrepreneur

-at

akhir

V→N

end

akhir-atthe afterlife

Table 5: Examples of Suffixes in Bahasa Melayu with Derived Forms
2.1.2

Suffixes

There are eight forms of suffixes in Bahasa Melayu and examples are shown in Table 5. Similar tothe prefixes, each suffix has its own function. For example, the suffix -an derive a noun kotoran “dirt”
from an adjectival root kotor “dirty”. It can also derive nouns from a verb root (e.g. makan “to eat” tomakanan “food”) or even from a noun root (e.g. ruang “space” to ruangan “a generic room”). Unlike
the prefixes, suffixes are more regular in their orthography. There are no occurrences of double suffixes
and allomorphs in any of the suffixes. However as shown in Table 6, clitics and particles can be added
to the end of words as case markers (Ranaivo-Malancon, 2004). Clitics and particles differ from affixes
in their functions as new words are not derived from them. They also vary in terms of position wherewords with prefixes can have clitics or particles added to it (Ranaivo-Malancon, 2004). For example, thederived word makanan can have the particle -ku attached to it to form makananku “my food”. However,prefixes cannot be attached to words that contain particles or enclitics (Ranaivo-Malancon, 2004). As
clitics and particles do not create new words, they must not be included as part of affixation in Bahasa
Melayu.
2.1.3

Circumfixes

The scope of circumfix in Bahasa Melayu can be broad to include all the derived words that areformed with both a prefix and an affix in them (See, 1980; Verhaar, 1984; Nakov & Ng, 2011). The
definition of circumfix can also be narrow, where a circumfix is only considered when the derived word

6
Enclitic/Particles

MarkerPossessive

Objective

–kau

x

x

–ku

x

x

–mu

x

x

–nya

x

x

–kah

x

Subjectve

Definitexx

Table 6: Examples of Enclitic and Particles in Bahasa Melayu with Derived Forms
Root form

With Suffix -an With Prefix ke- With Circumfix ke- -anboleh*bolehan*kebolehkebolehanlapanglapangan*kelapangkelapangan

Table 7: Circumfix in Bahasa Melayu
is invalid if either of the prefix or suffix was removed (Macdonald et al., 1976; Ranaivo-Malancon, 2004;
Sneddon, 2010). As shown in Table 7, both boleh and lapang do have a derived form with the circumfix
ke- -an. However, only the word lapang has the derived form with the suffix -an while *bolehan isconsidered to be invalid. As such, the word kebolehan is derived from the root word boleh with thecircumfix ke- -an while the word kelapangan is derived from the prefix ke- and the word lapangan;
which in itself comprises of the root word lapang and the suffix -an.
To create a standardised categorisation, this paper will use the definition by See (1980), Verhaar(1984) and Nakov & Ng (2011) and consider all words containing a combination prefix and suffix asa circumfix. Looking at the examples in Table 7, one could argue that the meaning of kelapangan“spaciousness” is much closer to lapang “spacious” than lapangan “field”. Therefore, the root word
for kelapangan should be lapang. This also enforced the fact that most forms of circumfix are different
from the stand-alone prefix and suffix as stated in Macdonald et al. (1976). For example, the prefix perderive agent or instrument nouns from verbs while the per- with the suffix -an derived nouns showing theprocess of the action from the root verb.

7
2.1.4

Infixes

Infixation in Bahasa Melayu occurs after the first consonant sound of the base word (Macdonald et al.,1976). The most common infix is -le- with words such as lelelaki “men” and kelelawar “bat” formed byadding that infix to the words lelaki “man” and kelawar “bat” respectively. However, studies have shownthat infixation is not productive in both Malay and Indonesian as compared to other related language like
Tagalog or Javanese (Macdonald et al., 1976; See, 1980). Studies also agreed that infixation is not part ofthe modern Indonesian language and new examples are hard to find (Macdonald et al., 1976; See, 1980;
Sneddon, 2010; Nakov & Ng, 2011) . As infixation is not productive, native speakers would usually
recognise it as part of the root word and not as a separate affix (Macdonald et al., 1976). Infixation canalso be argued as partial reduplication like in the example of lelelaki “men” where one can argue that
it is a partial reduplication of le- (Ranaivo-Malancon, 2004; Sneddon, 2010) . Also, the meaning of theroot and derived form can be similar as in the case of kelelawar and kelawar where both words referto the meaning “bat” (See, 1980). Therefore, as there are compelling evidence against the existence of
infixation in Bahasa Melayu , and that they are now discussed under root formation (Macdonald et al.,1976), it would be best to consider that infixation does not exist in modern Indonesian and Malay.
2.1.5

Transparency of Root Word to Derived Forms

In general, the derived forms can be easily linked to the root word via its definition or meaning. As
stated previously, the prefix peN- derives the person or instrument that does the action as described by
the root verb with the prefix meN-. For example, penulis “writer” is derived from the word menulis “towrite”. With the suffix -an, it shows the activity that arises from the verb, as in the word penulisan whichmeans “writing” (Macdonald et al., 1976). While the prefix ter- shows an accidental or uncontrollable
action of the root verb (Macdonald et al., 1976). For example, the derived word tertutup “unintentionallyclosed” comes from the root word tutup “closed”. As seen from the examples, the meaning of the derived
form would be close to the root form. Thus, as the definitions in dictionaries are comprehensive enoughto describe each word, comparing the definition of the root word to the derived forms should suffice todetermine the validity of the derived forms.

8

3Resources

3.1

Current state of Wordnet Bahasa

Wordnet Bahasa was created as an online lexical dictionary based on the Princeton WordNet (PWN)
created by Fellbaum (1998). The database contains the complete wordnet structure; with informationsuch as lexical relations (e.g. hypernym-hyponym) and meanings of particular synsets (mainly in English). As the name suggests, the Wordnet Bahasa combines both Bahasa Indonesia and Malay (Nurril
Hirfana, Suerya, & Bond, 2011). The two languages are joined under the generic Malay language code(msa) as they are mutually intelligible.3
Currently the Wordnet Bahasa contains 142,488 and 119,152 senses for Bahasa Indonesia and Malay
respectively. The senses were taken from dictionaries such as the French-English-Malay dictionary
FEM, Kamus Melayu-Inggeris KAMI, and aligned with wordnets from other languages (Nurril Hirfana,
Suerya, & Bond, 2011). The amount of senses that is available in Wordnet Bahasa would be sufficientfor formal links to be added as the extend of derivational morphology in the senses, will be explicit.
Thus, it would be vital to create the link between the root words and the derived forms to show a better
representation of how the words in Bahasa Melayu are linked through their morphology (Nurril Hirfana,
Suerya, & Bond, 2011). .
3.1.1

Derivational Morphology in Other Wordnets

In the Princeton Wordnet, the derivation link was first created for noun-verb pairs (Fellbaum, Osherson, & Clark, 2009). For example, the noun-verb pair of act-actor in the Princeton WordNet wasconnected via the derivational related forms link. The first focus was to look at the nouns derived fromverbs via the suffix -er/-or and then proceeded to other derivational nouns which ends in suffix such as-al, -ment and -ion. As shown in Table 8, the links were then categorised into eleven semantic categories (Fellbaum, Osherson, & Clark, 2009). However, in the current version of the Princeton Wordnet,
the derivational links are only categorized under the part of speech and not its semantic categories. A
comparative study of the English, Czech and Zulu wordnets was done in Bosch et al. (2008) where thecategorization and spread of derivation morphology was found to be more apparent in the Czech andZulu as the languages themselves contains more derived words.
3 A relationship between languages in which speakers of related language varieties can easily understand each other without

the need to learn each other’s language (Sugiharto, 2008).

9
Semantic Categories
Agent
Instrument
Inanimate agent/Cause
Event
ResultUndergoer
Body part
Purpose
VehicleLocation
Table 8: List of Semantic Categories used in Princeton Wordnet (Fellbaum, Osherson, & Clark, 2009)
The derivation link was also proposed in the wordnets pertaining to Slavic languages such as Bulgarian (Koeva, Krstev, & Vitas, 2008) and Serbian (Koeva, 2008). As derivational relations in the Slavic
languages are different than in English, creation of a semi-automatic derivational link generator was proposed in this paper. In Bulgarian, there is a pattern of nouns derived from adjective via suffix -oct or -ost.
As such, this link can be made by matching the root form of the noun to the adjective . The process was
not fully automated as some complex issues arose from it. For example, some of the derived words losses
their gender and thus, the word category changes (Koeva, 2008).
Bilgin et al. (2004) and Mititelu (2012) stated that using the semantic relations in another language
are an effective way of creating derivational links for the target language. Importing derivational links
from other languages will allow for a quicker set up (Bilgin, Cetinoglu, & Oflazer, 2004). In the final
stage, Bilgin et al. (2004) suggested for the derived morphology links to be categorized into the semantic
categorises or the result of the derived words.
During the creation of the derivational link in Romanian wordnet, the links were validated via an‘automatic heuristics’ process (Mititelu, 2012). As most of the root and derived forms in Romanian must
be in the same parts-of-speech (POS), those links which does not comply were considered as invalid.
Exceptions to the invalid data were picked out manually. A sample of 1000 links were chosen andmanually validated. The precision and recall of the links were tabulated and used to validate all thelinks that were created in the Romanian wordnet. Precision is the percentage agreement of relevant links

10SynsetMorphInd Analysis

MeaningmengawalmeN+kawal<v>

to control

bercepatber+cepat<v>

travel rapidly

urusanurus<v>+an

appointment

sebabnyasebab<v>+dia

originatekejujuranke+jujur<a>+an

honestymemperhalus

meN+per+halus<a>refineTable 9: Examples of MorphInd Process
while recall is the percentage of valid links that were retrieved from the sample data (Mititelu, 2012).
Mititelu (2012) argued that this method is the most effective way in creating the basic derivation link ina language.
Therefore, adding derivational links in Wordnet Bahasa will help to increase its usefulness as themorpho-semantic relations in Bahasa Melayu would be highlighted more clearly. The creation of derivational links in Wordnet Bahasa will be a modification of the methods created in the other wordnets. The
derivational links from another language (for example English) will be used in building the links for Bahasa Melayu. As this is the first attempt in creating the derivational link in Wordnet Bahasa, the validationof the link will be done semi-automatically similar to the process done in Mititelu (2012).
3.1.2

MorphInd

MorphInd is an open-source, Linux-based morphology tool which analysed the morphological structure of Indonesian words (Larasati, Kubon, & Zeman, 2011). This included derived words such as affixation, reduplication and compounding. MorphInd was an improvement over a previous morphologicalanalyser for Indonesian (IndMA) with a wider coverage of derivational and inflectional morphology andalso a much bigger database for its dictionary (Larasati, Kubon, & Zeman, 2011). Table 9 shows examples of how the MorphInd tool analysed a given word to its root word and affixes.
The Part of speech categorisation for Wordnet and MorphInd differs slightly, with nouns in MorphInd
separated in different categories as shown in Table 10. However, as MorphInd will only be used in thisstudy to breakdown the morphology of the senses in Wordnet Bahasa, the difference in categorisation will
not have any effect in this process. Ultimately, the Wordnet Bahasa categorisation will be used during
the linking process.

11
WordnetMorphInd

Noun (n)Noun (NN)Proper noun (NNP )Personal pronoun (PRP)
Foreign word (FW)Verb (v)

Verb (VB)

Adjective (a)Adjective (JJ )Adverb(r)Adverb (RB)

Table 10: Comparison of Wordnet Part of Speech Categories with MorphInd
Due to MorphInd’s high coverage of analysed words on an Indonesian corpus (at 84.5% out of 10,000
sentences) (Larasati, Kubon, & Zeman, 2011), it would be appropriate to include it as the first step ofanalysing the derivational morphology in Wordnet Bahasa. MorphInd will be used to break down thederived words into its root word and the affixes. The linking of the derived words to its root form will be
more efficient with this process. As the dictionary used in MoprhInd is different from Wordnet Bahasa,
not all of the senses in Wordnet Bahasa is expected to be be processed by MorphInd.

4

Methodology
The derivational links in Wordnet Bahasa are created by mapping the derived words to its root words.

Both the Indonesian and Malay data under Wordnet Bahasa were used. The derived forms must be
identified and processed to its root forms and affixes before the links can be created. This is done byusing MorphInd. The link creation process and other secondary processes (for example; cleaning ofdata) is done using Python 2.7 with NLTK (Bird et al., 2009).

4.1

Analysis of Wordnet Bahasa using MorphInd

Even thought MorphInd was used only on the Indonesian Corpus and was created to analyse the Indonesian language (Larasati, Kubon, & Zeman, 2011), the mutual intelligibility of Malay and Indonesian
would allow for the program to be used for Malay. As shown in table 9, the output given by MorphInd
splits the derived word into the root form and affixes. For example, the word urusan “dealings” will be12separate into the root form urus “deals” and the verbal suffix -an. The only exception for MorphInd isfor the suffix/enclitic -nya, where the tag +dia is used.

4.2

Mapping

The root words and the derived forms were matched by stripping off the affixes from the derived
words. For example, the sense nyata was mapped to the derived form ternyata. As MorphInd has
already analysed ternyata to ter-nyata<a>, the two words are mapped by stripping off the prefix ter- andthe POS tag <a>. However, as the links created only use the root form to match the two words, some ofthem might not be valid in Bahasa Melayu. This may be due to the root and derived forms being too farapart to be considered as a derivational link. Also, each word can contain multiple meanings which may
not match the derived forms. Over-generation of the derivational links will occur at this stage. Therefore,
there was a need to filter out the invalid links to ensure that only the correct senses are matched.
4.2.1

Using Wordnet Relations

The Princeton Wordnet has its own semantic relations under derivational_related_link (Fellbaum, Osherson, & Clark, 2009). One can test if the same relation occurs for Bahasa Melayu by using
this link that exist in Princeton WordNet. Another Wordnet relation that was applicable is pertainym(Fellbaum, Osherson, & Clark, 2009). Pertainym is a word (usually an adjective) which is related toanother word. For example, in the Princeton Wordnet, the adjective daily is a pertainym of the noun day.
The derivational related links will be expected to have a small impact on Wordnet Bahasa as English
is not as rich in derivational morphology as compared to Bahasa Melayu, As the derivational link in one
language can be used to prove the existence of derivational link in another language (Bilgin et al., 2004;
Mititelu, 2012), these two links will be used to prove that the derivational link does exist both in English
and Bahasa Melayu. Therefore, if a derivational_related_link or pertainym link from PWN canbe found in Wordnet Bahasa, this proves that the derivational link in Bahasa Melayu is valid.
4.2.2

POS Based Filter

As show in Table 1 to Table 5, all the affixes in Bahasa Melayu can only occur in certain Part-ofSpeech (POS) (Macdonald et al., 1976; Sneddon, 2010). However, unlike Romanian where the root andderived forms must be in the same POS (Mititelu, 2012), the POS for the root word and derived forms candiffer in Bahasa Melayu. Table 11 shows the breakdown of all available POS as described by Macdonald

13
et al. (1976), Verhaar (1984) and Sneddon (2010). For example, the prefix meN- can only form derived
verbs from root nouns, verbs or adjectives. A POS based filter is the most appropriate first step as thiswill ensure the right derived form is linked to the right root word. For example the word tegak can beused as a noun “upright” or an adjective “erect” or even as an adverb “straight”. However, the derived
word menegak “to erect” cannot be derived from tegak as an adverb. Furthermore, derived forms arealso subjected to the POS constraint. The derived form with the prefix meN- can only exist as a verb
in Bahasa Melayu (Macdonald et al., 1976; Sneddon, 2010). Without the POS filter, any derived words
with the prefix meN- that exist in the wrong POS (such as noun or adjective) will still be linked to theroot word. Therefore the POS filter will remove the redundant links and should increase the accuracy ofthe mapping.
4.2.3

Semantic Super Type Filter

Wordnet synsets were categorised into forty five different files super types on the syntactic category
and logical grouping of the synsets. As seen in Table 12, the spread of the semantic super types (alsoknown as Lexicographer Files) are more towards nouns and verbs. Using the semantic super types,
semantic categories for the root words and the derived forms are shown. As the prefix peN- will derive
an agent or instrument noun out of a verb (Macdonald et al., 1976; Sneddon, 2010), the derived formshould be under the super type of either personn or artifactn . As semantic features are more apparent
in nouns (Macdonald et al., 1976), the super type filter will be used on the derivation links with derived
noun forms. The affixes with concrete semantic categories and have suitable super types in Wordnet are
shown in Table 13. Using the semantic super type filter, the semantic type of the derived forms should
agree to the semantic features that are available in Bahasa Melayu as studied in Macdonald et al. (1976)
and Sneddon (2010).
4.2.4

Using Wordnet Definitions

As stated in the previous section, a word can contain many different meaning and also exist in different parts of speech. For example, the word hidup can exist as a noun, verb or adjective. As a noun, hidup
can have a number of definitions. Referring to Wordnet Bahasa, hidup can either be “a characteristic
state or mode of living” (WN ID:13963192-n) or “a state of surviving” (WN ID:13962166-n). While forthe derived form menghidupkan the definition can either be “cause to perform” (WN ID:00517529-v)
or “be brought back to life” (WN ID:00169298-v). Therefore, based on the definitions for hidup and

14

POS

AffixesNoun
N→N

per-, se-, pra-, anti-,-an, -wan, -wati, -i,
per- -an

N→V

di-, meN-, ber-,-kan,
meN- -kan, meN- -i, di-per-, di- -kan, di- -i, meN-per- -i, meN-per- -kan,
di-per- -i, di-per- -kan, ber-ke- -an, ber-per- -an

N→Ase-, -wan, -i,

N→Rse-, se- -nya

verb
V→N

peN-, ke-,peN- -an, ke- -an,

V→V

di-, meN-, ter-, per-, ber-,-kan,
per- -an, meN- -kan, ke- -an, ber-ke- -an

V→A

ter-

V→R

-nya, se- -nyaadjective
A→N

peN-, per-, ke-,-an,per- -an, ke- -an

A→V

ke- -anA→A

ter-, se-

A→R

se-,
se- -nyaadverbR→R

se- -nya
Table 11: Distribution of Affixes based on POS

15

Topsn

actn

animaln

artifactn

attributen

bodyn

cognitionn

communicationn

eventnfeelingn

foodngroupnlocationn

motivenobjectn

personn

phenomenonn

plantnpossessionn

processn

quantityn

relationn

shapenstatensubstancen

timen

bodyv

changev

cognitionv

communicationv

competitionv

consumptionv

contactv

creationv

emotionv

motionv

perceptionv

possessionv

socialv

stativev

weatherv

allapertainymsa

participiala

allr

Table 12: WordNet Lexicographer Files.
menghidupkan, the derivational link should be made for “a characteristic state or mode of living” and“be brought back to life” as they are connected by the word “life/living”. As such, to do this process
automatically, the definition of the words must be compared for its similarity. In this example, the similarity of “a characteristic state or mode of living” and “be brought back to life” should be higher than “astate of surviving” and “cause to perform” as there is a similar word in the first pair. Therefore, a string
similarity measure can be used to disambiguate the definition.4 Other components of the sense such as
its synonyms and examples can also be added as items of comparison.
The Lesk algorithm is a disambiguation method where the definition of each sense in a word iscompared to the definition of every word in a phrase (Lesk, 1986). A word is assigned to the mostappropriate sense which definition is similar to the definition of the other words surrounding it. For
example, using the phrase time flies like an arrow, the Lesk algorithm compares all the definitions of timeto the definitions of fly and arrow and assigns the most appropriate definition according their similarity.
As this algorithm can be used to compare the root words and derived forms, the Lesk algorithm will be
used for this study to evaluate the validity of the derivational links that are created from the raw mapping.
The Lesk Algorithm used for this study was adapted from Banerjee & Pedersen (2002) and Baldwin
et al. (2010) where an extended version was used. In the extended Lesk, the definition of the semantic
4 metric that measures the distance of one string to another.

16
POS→DPOS

Affixes

Semantic Features

Derived Super Type

N→N

per-

Agent or Instrument noun

personn , artifactnse-similar, singular, collection groupn or similar to rootpra-

previous state

similar to root-anvariety, collective

similar to root-wan

Agent Noun (male)personn-wati

Agent noun (female)personnper- -anProcess or Activity

actn

peN-

Agent or Instrument noun

personn , artifactn

per-Agent or Instrument noun

personn , artifactn-anresultative, undergoer,actn , communicationn ,or Instrument noun

personn , artifactn , foodnProcess or Activity

actn

peN- -an Process or Activity

actn

V→N

per- -an

A→N

peN-

Agent or Instrument noun

personn , artifactn

per-Agent or Instrument noun

personn , artifactnpeN- -an Process or Activity

actn

Table 13: Suitable Semantic Features using Wordnet Super Types
relations (e.g. hypernym-hyponym relations) in the word was added to word definition during the comparison process (Banerjee & Pedersen, 2002). Baldwin et al. (2010) refined the algorithm even further bylooking at the definition of each word that exists in the main word’s definition. For example, the definition of the word goal in PWN is “a successful attempt at scoring” (WN ID:00187337-n). The extended
Lesk algorithm by Baldwin et al. (2010) will take the definition of successful, attempt and scoring on topof the main definition of the word. These extended definitions can be taken from the Princeton WordNet
Gloss Corpus.5 The Gloss Corpus used the definition that exist in the Wordnet synsets and manually linkeach word in the definition to the most appropriate sense in Wordnet (Szymański & Duch, 2012). Therefore in the Gloss Corpus, the Princeton Wordnet acts as the dictionary in which the definition from thesame Wordnet is tagged. This was similar to the spreading activation process where during sentence comprehension, words in the sentence are automatically linked to the related concept (Szymański & Duch,
5 http://wordnet.princeton.edu/glosstag.shtml

172012). This could serve as a rough semantic representation of the target sense as the tagging process in
the Gloss Corpus is constrained by the other senses surrounding it. Thus, adding the Gloss definition on
top of the sense definition, hypernyms and hyponyms will increase the accuracy of representation for theparticular sense.
Three different measurements are created for this study to ensure that the different components of a
Wordnet sense are covered and to get the most accurate measurement for the similarity of the root andderived form. Lesk 1 (Algorithm 1) measures the similarity of the lemmas, examples and definition. Lesk
2 (Algorithm 2) uses the sense’s definition, hypernyms and hyponyms while Lesk 3 (Algorithm 3) uses
the definitions, hypernyms, hyponyms and Gloss definition. Even though the three string measurements
uses different items, there should be similarity in them regarding the appropriateness of the root andderived forms. If the root and derived forms are very far apart (where the meaning is not transparent), thescores for all the three measurements should be lower than when the two forms are related.
for each synset in wordnet dofor each sense and examples and definition in synset do
string = lemmas+examples+definition
end
score(main,derived) = similarity(stringmain ,stringderived )end
Algorithm 1: Lesk 1=synonyms+examples+definition

for each synset in wordnet dofor each definition and hypernyms and hyponyms in synset do
string = definition+hypernym+hyponym
endscore(main,derived) = similarity(stringmain ,stringderived )end
Algorithm 2: Lesk 2=definition+hypernyms+hyponyms

18

for each synset in wordnet dofor each definition and hypernyms and hyponyms in synset dofor definition in Gloss_Corpus do
if definition in synset == definition in Gloss_Corpus thenGloss_definition = definition of synset in Gloss_Corpus
end
end
string = definition+hypernyms+hyponyms+Gloss_definition
end
score(main,derived) = similarity( stringmain , stringderived )end
Algorithm 3: Lesk 3=definition+hypernyms+hyponyms+Gloss_definition

5

Results

5.1

MorphInd Process

MorphInd was able to analyse the senses in Wordnet Bahasa into the root words and affixes. Table 14
shows the breakdown of the MorphInd process on Wordnet Bahasa’s senses. As MorphInd uses its own
dictionary for analysis, only 70% of the total number single word senses is processed by the tool asshown in Table 14. The borrowed prefixes found in Wordnet Bahasa are anti- and pra- . MorphInd also
processed words with the particle -lah and the clitic -nya and futher breakdown of the MorphInd process
into the different affixes are shown in Appendix C . For the suffix -nya, derived forms will only occur
from a verb root or as a circumfix with a prefix se- (Macdonald et al., 1976; Verhaar, 1984). While thosesense with the particle -lah and the clitic -nya will not be linked to the root word as they are part ofinflectional morphology Ranaivo-Malancon (2004).
5.1.1

Derivational Link From Princeton Wordnet

The derivation_related_link and pertainym link from the Princeton Wordnet did apply to
Wordnet Bahasa as shown in Table 15 . The number is quite minute as compared to the links present
in the whole data. However, the presence of the Princeton Wordnet derivational links as shown in Table 15 affirms the derivational links in Wordnet Bahasa especially for nouns and verbs (Bilgin et al.,2004). Those Wordnet Bahasa sense with either the derivational or pertainym link from the Princeton

19

Wordnet senses

IndonesianMalay

142,488

119,152

Processed

Not Processed

Processed

Not Processed

Multi-word

23,261

13,271

20,036

11,146

Single-word

71,042

34,914

63,386

24,584root word

25,64523,634Prefix

18,405

15,934

Suffix

5,127

4,878

Cirumfix

21,837

18,913

Table 14: Result of MorphInd Process
Wordnet as shown in Table 16 will be considered as a concrete derivational link.

5.2

POS and Semantic Super Type Filter

A raw mapping is created between the root and analysed words. This is done for all POS in the
Wordnet Bahasa and the number of links is shown in Table 17. Overall, root verbs have the highest
number of derived forms followed by nouns, adjective and adverbs for both databases.
As seen from the Table 17, the POS filter helped in reducing the redundancy of the derivational
links. The number of links decreases for all the POS for both Indonesian and Malay data. As statedpreviously, derivational forms would only arise from certain POS (Macdonald et al., 1976; Sneddon,2010). Examples of the validated links are shown in Table 18. The link betweeen nafas and pernafasan
was valid as the POS filter will allow a noun to be derived from a verb via the circumfix per- -an. Whilefor terbelakang and bertahan, the links are invalid as the prefix ter- and ber- cannot derive nouns fromverbs. For the derived verb mencuba, the link to cuba was invalid as verbs with prefix meN- cannot bederived from adverbs. The semantic super type filter also eliminates those links with the derived formin the incorrect super type category as shown in Table 19. The super type filter can only increase theaccuracy of derived nouns, as concrete semantic categories are only found in this category (Macdonald
et al., 1976; Sneddon, 2010). However, the super type filter was still effective as it was able to reduce thenumber of invalid links for the derived noun categories as shown in Table 17.

20
Indonesian
POS→DPOS

Malay

Derivational

Pertainym

Derivational

Pertainym

N→N

547

0

501

0

N→V

2,398

0

2,156

0

N→A

10

0

9

0

N→R

1

0

1

0

V→N

1,262

0

1,200

0

V→V

1,677

0

1,403

0

V→A

647

0

695

0

V→R

0

0

0

0

A→N

573

18

552

19

A→V

2

0

2

0

A→A

0

0

0

0

A→R

0

0

0

0

0

0

0

0

noun

verb

adjective

adverb
R→R

Table 15: Princeton Wordnet Derivational and Pertainym Links in Wordnet Bahasa
5.2.1

Using Wordnet Definitions

The different components of the synset (e.g. definition. hypernym, hyponym, examples) have to beconcatenated into a string before the string similarity measurement can be done as shown in Table 20.
For example in Lesk 1, the first string will consist of synonyms, examples and definition of the root
word while the second string will consist of synonyms, examples and definition of the derived form. The
strings are cleaned up of all punctuation such as commas and full stop. These punctuations usually exist
in the definitions and examples, and may affect the string measurement if they are not removed.
A similarity string measurement will use the two strings that were generated and return a score of theirsimilarity. As with the extended Lesk Algorithm that was used by Baldwin et al. (2010), this paper also

21
POS→DPOS

A→N

V→N

A→N

WN ID

Sense

From Eng Wordnet

02460964-a

jati

Root: genuine

13955341-n ke-jati-an

DRV: genuineness02024367-v meN-tekan

Root: press

03999992-n peN-tekan

DRV: press

02743261-a

Root: human

04829182-n ke-manusia-an PRT: humanity
02767378-a

A→N

manusialahir

15142167-n ke-lahir-an

Root: birth
PRT: birth

Table 16: Wordnet Bahasa Derivational Links with Princeton Wordnet Equivalentd=2(|X ∩Y |)|X| + |Y |Figure 1: Dice Coefficientuses the Dice coefficient as a similarity measure. The cosine string measure was first tested in this study
but the score generate was insignificant and irregular. Dice coefficient is a metric used to compare twostrings in terms of common bigrams (pair of adjacent letters) in a string. The formula for Dice coefficientis shown in Figure 1. In this formula, X is the common bigrams of the root word while Y is the common
bigrams of the derived form. d is the quotient of similarity, with the score of 1 begin the most similar and
0 for no similarity between the 2 strings.
To achieve the best accuracy during the WSD process , a “Gold standard” sample data is created.
A “Gold standard” is a standard accepted as the most valid for the particular study and in this case, themost valid derivational links that were created in Wordnet Bahasa. Boyd-Graber et al. (2006) stated that
researchers agree more with a manual tagged “Gold standard” than a fully automated version. A sample
size of 966 sense link was manually tagged. The Lesk 1 string of the root word and the derived forms
were used to determine the validity of the links. The objective of the threshold level is to find the bestscore with the highest correct pairing (also known as precision). Even with the use of the POS and supertypes filters, errors exist in the links. This is mainly caused by the mismatch of the definition to the rootand derived forms. These errors are tagged as "D" and "R" to indicate error in derived definition and root
definition respectively and are excluded. Those which are considered for analysis, are either tagged as22Indonesian
Derived form

Malay

Raw

Pos

Super Type

Raw

POS

Super Type

N→N

28,07913,874

3,802

26,333

13,051

3,603

N→V

67,98465,44665,44655,44353,33153,331

N→A

8,183

565

565

8062

498

498

N→R

898

229

229

845

225

225

V→N

76,87059,72046,18573,43556,75844,356

V→V

97,21485,16285,43875,63575,63575,635

V→A

5,070

647

647

5174

695

695

V→R

514

93

484

484

84

84

A→N

33,28329,60527,56831,88928,531

26,643

A→V

52,252369

369

45,817

361

361

A→A

6,067

242

242

6,348

223

223

A→R

1,810

240

240

1,753

255

255

R→N

3,108

0

0

3,058

0

0

R→V

8,612

0

0

7,796

0

0

R→A

992

0

0

1,017

0

0

R→R

595

69

69

585

72

72

noun

verb

adjective

adverbTable 17: Breakdown of Mapped Derivational Links before and after Filters"Y" for valid or "N" of invalid link. In total, 693 links were considered valid out of 966 sample senses link
and the threshold level (precision score) is determined using the recall of “Y” and “N” . The precision
score and sample recall are shown in Table 21 to Table 24.
Out of the three Lesk measurements, Lesk 3 has the best sample recall of 481 Y-Y pairing and
1 N-N pairing. As the three Lesk algorithms measure different components of a sense, there was no
improvement in the precision or recall when they are combined as shown in Table 24. As such, Lesk
3 is used as the most appropriate similarity measurement with the threshold value of 0.149. After the

23
POS→DPOS WN ID
14841770-n

Sense

Definitionnafas

the air that is inhaled

Validityand exhaled in respiration
N→N

00831191-n per-nafas-an the bodily process of in-Validhalation and exhalation
00061203-r

belakang

happening at a time
subsequent to a reference time

N→N08502507-n ter-belakang

a place or condition in

Invalidwhich no development
or progress is occurring00459776-v

tahan

cause to be slowed
down or delayed

V→N10303654-n ber-tahan

someone who exhibits

Invalid

great independence inthought and action
00004722-r
R→V

cuba

02373336-v men-cubaand nothing more
proceed somewhere de-Invalidspite the risk of possible
dangers
Table 18: Validation of Derivational Links via POS Filter
baseline threshold level was set, an automated word sense disambiguation (WSD) process was done tosee the recall of the Lesk measurements and the validity of the derivational links across both the Malay
and Indonesian data. The results of the WSD process are shown in Table 25.
For comparison purposes, each of the Lesk measurements’ threshold level is parsed through both theIndonesian and Malay data of Wordnet Bahasa. As seen from table 25, the recall level for all the Leskmeasurements are really high with most of them giving a score of 0.90 or above. This means that 90%of the links are considered as valid. This was due to the very low threshold levels that were set for all
the three Lesk. Nevertheless, when the Lesk measurements were compared with each other, Lesk 3 has

24
POS→DPOS WN ID
06723908-n
N→N

V→N

N→N

N→N

Super Typekatacommunicationn

07009421-n pra-katacommunicationn

00259755-v

meN-usikchangev

10305192-n peN-usik

personn

01217499-n

actn

wakil

01140839-n per-wakil-an actn
13828075-n

N→N

Sense

arah

communicationn

04998530-n

attributen

04683814-n rupa-wan

Valid

Valid

Valid

relationn

06786629-n arah-an
rupa

Validity

attributen

Invalid

Invalid

Table 19: Validation of Derivational Links via Super Types Filter
the lowest recall level for most of the POS and derived form. On the other hand, Lesk 1 has the highest
recall with a score of 1.00 for adverbs derived from verbs for both Malay and Indonesian. This confirmed
Lesk 3 as the most appropriate string measurement of the derivational links. However, with the high levelof errors as stated above, they render the Lesk algorithm ineffective in disambiguation the derivational
links.

25

Example04617562-n

ke-peribadi-an

Lesk 1synonympersonality

Definition

the complex of all the attributes behavioral temperamental emotionaland mental that characterize a unique individual

Examplestheir different reactions reflected their very different personalities it is
his nature to help others

Lesk 2Hypernym

attribute

Hyponyms

narcissistic personality identity oral personality genital personality anal
personality personableness obsessive compulsive personality

Definition

the complex of all the attributes behavioral temperamental emotionaland mental that characterize a unique individual

Lesk 3Hypernym

attribute

Hyponyms

narcissistic personality identity oral personality genital personality anal
personality personableness obsessive compulsive personality

Definition

the complex of all the attributes behavioral temperamental emotionaland mental that characterize a unique individual

Gloss Definition

highly unusual or rare but not the single instance relating to or causedby temperament of or relating to behavior a human being involving themind or an intellectual process the complex of all the attributes behavioral temperamental emotional and mental that characterize a unique
individual of more than usual emotion

Table 20: Breakdown of the Different Lesk Strings
ComputerThreshold 0.144 Y

N

Y

481

1

N

15

1

D

22

R

153

Human

Table 21: Result of Sample using Lesk 126ComputerThreshold 0.044 Y

N

Y

479

3

N

153

3

D

22

R

153

Human

Table 22: Result of Sample using Lesk 2ComputerThreshold 0.149 Y

N

Y

482

0

N

155

1

D

22

R

153

Human

Table 23: Result of Sample using Lesk 3ComputerThreshold 0.044 Y

Human

N

Y

481

1

N

155

1

D

22

R

153

Table 24: Result of Sample by Combining all Lesk Results

27Indonesian

Malay

MeasureLesk 1

Lesk 2

Lesk 3

Lesk 1

Lesk 2

Lesk 3Threshold

0.144

0.044

0.149

0.144

0.044

0.149

N→N

0.995

0.999

0.999

0.995

1.0

0.999

N→V

0.990

0.993

0.991

0.989

0.994

0.991

N→A

0.992

0.948

0.961

0.992

0.975

0.958

N→R

0.986

0.973

0.995

0.986

0.977

0.968

V→N

0.989

0.994

0.988

0.989

0.995

0.988

V→V

0.991

0.986

0.976

0.991

0.987

0.975

V→A

0.991

0.979

0.984

0.997

0.978

0.985

V→R

1.00

0.989

0.989

1.00

0.988

0.988

A→N

0.987

0.986

0.986

0.992

0.987

0.986

A→V

0.99

0.975

0.959

0.994

0.974

0.96

A→A

0.995

0.983

0.971

0.995

0.982

0.972

A→R

0.988

0.954

0.975

0.988

0.951

0.972

1.00

0.898

0.913

1.0

0.892

0.909

Noun

Verb

Adjective

Adverb
R→R

Table 25: Recall Value using Lesk Measurements

28

6Discussion
Errors in the MorphInd process were found in this study. For example, words like sebarang “any-thing” and pemarah “angry person” were separated into se+barang and peN+parah. For the word sebarang it is considered a word unit and not a prefix se- with the root word barang. Native speakers would
agree that sebarang “anything” is not derived from barang “item” as the definition for the two words arequite far apart. Regarding the word pemarah, the analysed form is wrong, as it should be peN+marah instead of peN+parah. This causes the word to be linked to parah instead of marah and therefore, causing
the link to be invalid. These errors are highlighted to the creator of MorphInd so that they can be rectified
in the future release of the software. Further improvements to MorphInd are explained in Appendix B.

6.1

Evaluation of POS and Super Type Filter

The POS filter is much more comprehensive as compared to the Super Type filter. The POS filter
encompasses the whole data and was able to remove derivational links with the root and derived forms in
the wrong POS. For Wordnet Bahasa, the Super Type filter can only filter out those affixes with concrete
semantic features in their derived form. Thus, the Super Type filter is only able to filter out only thederived nouns because of this limitation. Also, as the distributions of the super types are not even for all
POS in Wordnet, it is difficult to use the filter for other part of speeches. This is especially true in thecase of adjectives and adverbs where most of them are grouped in one major type - alla and allr . Thus,
it would be difficult to differentiate the types of adjective and adverbs using the super types. However,as both filters were able to increase the accuracy of the derivational link by reducing the redundant
and unwanted entries through POS and semantic features, the filters are useful to the creation of thederivational link in Wordnet Bahasa. As the redundant and error links are removed, the links will become
more representative of the derivational morphology in Bahasa Melayu.
Further filters can be added to enhance the accuracy of derivational links in Wordnet Bahasa. One
area that can be studied is transitivity, where the number of objects a particular verb can hold is analysed.
As change in transitivity was involved when deriving a verb from a root verb using the prefix meN- anddi- (Macdonald et al., 1976), the wordnet Verb Frames can be used to analyse the transitive link. The
Verb Frames that represents transitive verbs are shown in Table 26. For the prefix di-, it cannot be derived
from transitive root verbs (Macdonald et al., 1976). Therefore using Verb Frames, those derived forms
with prefix di- and are link to a transitive root verb will be removed from the database. However, as the29
Frame No.

Verb Frame

8

Somebody —-s something

9

Somebody —-s somebody

10

Something —-s somebody

11

Something —-s something

12

Something —-s to somebody

13

Somebody —-s on something

14

Somebody —-s somebody something

15

Somebody —-s something to somebody

16

Somebody —-s something from somebody

17

Somebody —-s somebody with something

18

Somebody —-s somebody of something

19

Somebody —-s something on somebody

20

Somebody —-s somebody PP

21

Somebody —-s something PP
Table 26: Verb Frames indicating Transitive Verbs

transitivity filter will only be used for the prefix di- and that the number of derived words with the prefix
di- in the data is small, using this filter now will not have a big impact in disambiguating the links.

6.2

Evaluation of WSD using Lesk algorithm

Good examples in the derivational links can be seen in Table 27. The word relatif has a derived form
which is above the Lesk 3 threshold of 0.149. Furthermore, the derived form kerelatifan has a similar
derived form relativity in the Princeton Wordnet. While the word asuh has a derived form asuhan witha pertainym link education in English and a Lesk 3 value of 0.521. As such, this links are considered
concrete examples of good derivational link in Wordnet Bahasa. However, as stated before, the thresholdlevel set by the “Gold standard” is very low for all the Lesk algorithms. This caused a substantial numberof erroneous links to be passed as valid links, even for the best Lesk algorithm - Lesk 3. Three main
reasons can be attributed to the low threshold levels.
Firstly, there is a mismatch in Wordnet Bahasa with regards to the sense and its definition. This error

30
POS→DPOS WN ID
00006032-a
A→N

PWN

Lesk 3

DRV: relativity

0.351

PRT: education

0.521

relatif

06106502-n ke-relatif-an
02946221-a

A→N

Sense

asuh

04921900-n asuh-an

DRV: education
00004475-n

manusia

N→N04726938-n ke-manusia-an None

0.533

N→N

04829182-n ke-manusia-an None

0.40400002684-nbarangN→R00024509-r*se-barang

None

0.352

N→A

02267686-a*se-barang

None

0.332

00004227-v

*mati

V→R

00024509-r*ke-mati-an

None

0.22

V→A

02267686-a*ke-mati-an

None

0.26

00010435-v

tindak
None

0.509

V→N

06532095-n *tindak-an

Table 27: Evaluation of Derivational Links after Filters and Leskis still quite apparent in Wordnet Bahasa where some words are misrepresented in the wordnet due to theambiguity of the English translation. thus causing wrong links to be created. For example, in Table 27,
the definition of the root word mati (WN ID:00004227-v) is “to expel air”. As mati can only relate
to “endings” or “death”, it was wrongly added to the particular definition. This caused the link to theany of derived word of mati to be invalid (tagged as “R” in the sample data). Another example would be
the word tindak (WN ID:00010435-v) and its derived word tindakan (WN ID:06532095-n). The derived
definition is "a legal document codifying the result of deliberations of a committee or society or legislative
body". Even though morphologically the link is correct, the discrepancy between the derived word anddefinition causes this link between the two words to be invalid (tagged as “D” in the sample data) . As thedefinition is one of the main items tested for all three Lesk algorithms, this causes a huge number of noise
to be present in the data consisting of either wrong root definition or wrong derived definition. Even as
the error links were removed during the “Gold Standard” test, there was no significant improvement to31threshold level. As seen from Table 21 to Table 24, the errors in root words (tagged as “R”) and derived
forms (tagged as “D”) make up almost 20% of the links in the sample data. As shown in Table 27, theDice coefficient score for the invalid links are still higher than the threshold level and this is even after
they were removed from the “Gold Standard” test. Therefore, it is very likely that the error entries areextensive enough to render the Lesk method to be ineffective.
Another reason for the WSD process to be ineffective is that the ambiguity between the Bahasa
Melayu word and English definition. Translated text might not be true representation of the intended
meaning, so much so that the sense in one language can differ in meaning in another (Uchida & Zhu,
2001). For the word memotong in Wordnet Bahasa, one of the definitions is “end or extinguish byforceful means”. This was incorrect as memotong actually means “to cut” or “to slice”. Therefore,
the English definition for the word sense does not suit that particular word. However, when the Leskalgorithm was used to measure the string similarity for Lesk 3 of the root word pemotong to the derived
form memotong, the Lesk 3 score was 0.546. This is way above the threshold level of 0.149 for Lesk
3. Therefore, adding and using Malay definitions may solve this issue as the ambiguity element will be
removed.
Finally, errors in the MorphInd analysis affect the Lesk measurement. Even though the words barang
and sebarang passed the POS filter, they are invalid in actual fact as their definition was very far apart.
Interestingly, the Lesk 3 score for this link ( 0.352) is not much different from the Lesk score for the valid
link relatif to kerelatifan (0.351). Thus, this caused the Dice coefficient measure and Lesk algorithm asa whole to be ineffective as it was unable to make the distinction between the valid and invalid links.

7

Further WorkAs seen in the results section, the amount of noise in Wordnet Bahasa did affected the WSD process.

This causes the WSD process to be unable to disambiguate the wrong links from the correct ones. As
the Lesk score for the wrong link is higher than or similar to the threshold level they are deemed as valid
links. Steps were taken to try and improve the score such as eliminating those with wrong definition for
the root or derived forms. However, even with these steps, very little improvements can be seen in the
precision and recall values.
Therefore, the Wordnet Bahasa has to be cleaned further before the derivational link can be added.
Tools such as a morphological analyser can be used to identify words in the wrong POS. As shown by the

32
POS filter, the part of speech of a derived word is determined by the affixes. In this study, the MorphInd
tool was able to extract a total of 6,500 wrong entries of affixed single words for both the Indonesianand Malaysian data. However, more can be done to improve the Wordnet Bahasa. As MorphInd targets
Indonesia words, the Malay data can be further improved by using morphological analyser that is specificfor Malay. With more morphological analysers being used, more wrong entries can be identified and thusincreasing the accuracy of the Wordnet Bahasa.
Currently, Wordnet Bahasa uses the English definitions as found in the Princeton Wordnet. This
was adequate for activities such as corpus tagging, as the definitions are use only to identify the mostapportioned sense. However, in this study, the definitions were used for comparison during the WSD
process. As information is always lost in translation (Uchida & Zhu, 2001), the English definition will
not fit the Malay word fully. As stated above, misrepresentation of the Malay word in Wordnet Bahasa
was caused by this ambiguity. For example, the current definition for tembak (WN ID:02123175-v) is"cause a sharp and sudden pain in". Even though tembak is a direct translation of the lemma "shoot",
the more appropriate sense in Bahasa Melayu would be cucuk. Thus, with the addition of the Malay
definitions, the differences in meaning would be clearer and it will be more representative of the senses
using the same language. Furthermore, the misalignment of the Malay sense to the English definition
will be more explicit and this will assist in removal of the invalid senses.
Another method of disambiguating is via corpus tagging process. Similar to the “Gold Standard” test,
the targeted affixed word would be tagged with the most appropriated root word and derived form. In
this way, the link between the root and derived word can be firmly established manually. Even thoughtagging is not efficient and time consuming, it would be one of the most effective way of establishingthe derivational links. As a native speaker would be using his/her native knowledge of Bahasa Melayu
to do the tagging process, the validity of the derivational links through this process will be much more
concrete.
On top of affixes, Bahasa Melayu has other derivational word formations such as compounding andreduplication. These two word formations should be processed similarly to the affixed words. As MorphInd is able to handle these two word formation process, the software can still be used to analyse thederived words. However, these two processes should be looked at when the links for the affixed words
are improved to an acceptable level. The semantic categories should also be added to better represent thefunction of the derived words (Fellbaum, Osherson, & Clark, 2009).

33

8Conclusion
This study was able to create a basic derivational link for the root and affixed words in Wordnet

Bahasa for both the Malaysian and Indonesian data. Using the MorphInd, derived words were broken
down into its root word and affixes. This process helped in the raw linkage of the root words to theanalysed derived word. These links were then refined using the POS and semantic Super Type filter.
However, when the automated WSD process was executed via the Lesk algorithm, it was proven to beineffective. One of the main factors is the high amount of noise that is still in Wordnet Bahasa. Therefore,
because of the high level of inaccuracy and noise, it would be inappropriate to include all the derivational
links into the Wordnet Bahasa for now. As stated above, only those links with a derivational or pertainym
link in English will be included into the Wordnet Bahasa. The wrong derived words that were discovered
using MorphInd are removed from the Wordnet Bahasa. The rest of the links are released as an external
file and will be included in the Wordnet Bahasa website. The “Gold standard” test that was done prior to
the automated process is also release with the definition and tags clearly shown.
In conclusion, by attempting to create the derivational link in Wordnet Bahasa, errors were discovered in the Wordnet Bahasa. Even though the study did not achieve the full automated WSD process
for the derivational link in Wordnet Bahasa, basic links for affixes words were created. Some of thelinks are also supported by the existence of the same derivational link in the Princeton Wordnet. As, itwould be inappropriate to add all the derivational links to the current Wordnet Bahasa, they are released
as a separate file as shown in Appendix A. With the MorphInd tool, most of the derived words werebroken down it its affix and root word. Additionally, MorphInd is able to extract the error words from
Wordnet Bahasa.and thus, assisting in the clean-up of the dictionary. The accuracy of Wordnet Bahasa
will increase progressively with the database cleaned up and addition of Malay definitions. Thus, after
improvements made to Wordnet Bahasa, the Lesk algorithm (or its equivalent) can be used effectively indisambiguating the derivational links and a more representative data of the derivational link can then be
added into Wordnet Bahasa.

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Appendix A

ReleaseIn total, using the MorphInd tool, there are 3,255 and 3,299 wrong entries in Wordnet for the Indonesia
and Malay data respectively. They will be removed from Wordnet Bahasa. As compare to the number ofsynsets available in Wordnet Bahasa, this accounts for 2% of the whole database. Through the MorphInd
analysis, the derived words contained in this list are incorrect because they exist in the wrong POS.
Taking an example from Table 28, mendatangkan cannot be a noun as the circumfix me- -kan can only
derive verbs.
WN ID

Sense

00001740-v

pernafasan

00003829-s

melahirkan

00003846-r

menindih00004032-v

keluhan

00007347-n

mendatangkan

00007347-n

menetaskan

00007347-n

mengakibatkan

00007347-n

menimbulkan

00021766-a

ketepatan

00024720-n

menyatakan

Table 28: List of Error Words

37

POS→DPOS

Affixesroot WN ID

derived WN ID

Lesk1

Lesk2

lesk3

Tag

PWN

A→N

ke++an00631391-a

04899201-n

0.463

0.496

0.434

Y

DRV: correctness

A→N

ke++an00631391-a

04803209-n

0.369

0.252

0.305

Y

None

V→N

peN+

02302220-v

10180178-n

0.367

0.272

0.256

Y

None

V→N

peN+

02302220-v

03485997-n

0.5

0.271

0.247

Y

None

V→V

ter+

01621555-v

01494310-v

0.414

0.591

0.699

Y

None

A→N

ke++an02386612-a

14450339-n

0.452

0.207

0.228

Y

DRV: shortness

A→N

per++an

02748635-a

00923444-n

0.448

0.37

0.622

Y

PRT: industry

A→N

ke++an00964470-a

06212422-n

0.343

0.16

0.521

Y

None

Table 29: Example of Automated Derivation Link in Wordnet Bahasa
As the level of inaccurate derivational links was substantially high, it is inappropriate to include all thelinks into the current Wordnet Bahasa. Only those which are verified by the Princeton Wordnet throughthe derivational and pertaiym link will be added into Wordnet Bahasa as these are considered to be goodand concrete links. For the rest of the automatic links, they will be release as an external file showing theroot ID, derived ID and the affix that links these two IDs as shown in Table 29.
The evaluation data set or “Gold standard” will also be released. This will consist of the tags, WN
ID and sense and the string used for comparison (Lesk 1) as shown in Table 30. This data will show how
the links were adjudicated by the experimenter. Further studies can look at this data set and evaluated if
the tagging process was done appropriately.
Similar to Wordnet Bahasa, these data sets will be posted onto the Wordnet Bahasa website.6 They
are also released under the MIT license.7 This allows of usage, modification, publish and even selling of
the data with the proper acknowledgement given.

6 http://wn-msa.sourceforge.net/
7 http://www.opensource.org/licenses/mit-license.php

38

Tag WN ID
00956131-a

Sense

Lesk 1 String

jujur<a>fair just free from favoritism or self interest or bias or deception
conforming with established standards or rules a fair referee fairdeal on a fair footing a fair fight by fair means or foulY

04867130-n

ke+jujur<a>+ansincerity the quality of being open and truthful not deceitful or
hypocritical his sincerity inspired belief they demanded some
proof of my sincerityN04650731-n

ke+jujur<a>+an

frankness outspokenness the trait of being blunt and outspoken

N04871720-n

ke+jujur<a>+an

candor candour candidness frankness directness forthrightness thequality of being honest and straightforward in attitude and speechY04871374-n

ke+jujur<a>+an

honesty honestness the quality of being honest

D

04701943-n

ke+jujur<a>+an

pellucidness pellucidity limpidity passing light without diffusion
or distortion

Table 30: Example of “Gold Standard” data Release

Appendix B

Evaluation of MorphInd

As mentioned before, the MorphInd tool was able to analyse all the affixes including clitics andparticles that exist in Wordnet Bahasa. This allowed for the affixed words to be fully linked to all the rootwords. Those senses with clitics and particles, as detected by MorphInd, were removed from the linkage
process. However, because of the difference in the dictionary used by MorphInd, not all the senses were
processed. Attempts were made to add Wordnet Bahasa senses to the MorphInd’s dictionary but they
were unsuccessful as only the binary version of the dictionary was available in the public release versionof MorphInd. The creator of MorphInd was contacted regarding this issue via email and the developer’s
version of MorphInd was given. With the developer’s version, the dictionary can be edited under the
file lemma.lexin with the format as shown in Table 31. Thus, by improving and increasing the sizeof the dictionary as more words will be analysed and linked. This will create more derivational links
in Wordnet Bahasa during the raw mapping process. However, this process should be done after the
Wordnet Bahasa has been cleaned as there are a number of Bahasa Melayu senses in the wrong POS inthe current version. A such, adding the senses from the current version will also increase the number ofwrong entries in MorphInd.

39
adjectivekadekuat
adjectivekademforeignkdubinsky
foreignkdublin
foreignkducati
nounkdwitunggal
nounkebek
nounkebi
verbkjunjung
verbkkabruk
verbkkabung
verbkkabur
Table 31: Format of MorphInd Dictionary

Appendix C

MorphInd Process by Affixes

Affix

Indonesian

Malay

meN-per-254

127

ber-peN-23

10

ter-peN-1010di-per-

1

1

Table 32: Result of MorphInd Process for Double Prefixes

40

Affix

Indonesian

Malay

meN-

11,121

9,364

ber-

4,046

3,629

peN-

1,549

1,508

ter-

963

849

se-

166

167

di-

162

159

ke-

30

31

pra-

18

14

anti-

8

5

-an

3,770

3,565

-kan

680

673

-i

265

249

-nya*

225

228

-wan/-wati

61

52

-lah*

17

23

Table 33: Result of MorphInd Process for Single Affixes

41

Circumfix

Indonesian

Malay

meN- -kan

9,832

8,364

ke- -an

3,889

3,586

meN- -i

2,598

1,968

per- -an

1,348

1,271

meN-per- -kan

556

392

ber- -an471

358

di- -kan

202

105

meN-per- -i

109

78

di- -i66

73

ber- -kan

64

58

ber-ke- -an

42

31

se- -nya

35

49

peN- -an29

5

ber-peN- -an

23

10ber- -i1111ter- -kan

912

ter- -nya*

77

ber-per- -an

77ber- -nya*

22ke- -an22di-per- -kan

22

di-per- -i

1

1

ke- -nya*11ter- -i11meN- -nya*

1

1

Table 34: Result of MorphInd Process for Circumfixes