International Journal of Turkish Education and
Training Uluslararası Türkçe
Eğitimi ve Öğretimi Dergisi Sayfa:1-17
Sayı/Volume: 2 Yıl/Years 1
ISSN: 2458-9462
Türkçe İçin Otomatik Zıt Anlamlılar Sözlüğü Oluşturma Aracı
(AADICT)
Automated Antonym Dictionary Generation Tool for Turkish
(AADICT)
Çağdaş Can Birant1, Özlem Aktaş2, Özgün Koşaner3, Belgin Aksu4, Yalçın Çebi5
Özet
Bu çalışmada, Türkçe için geliştirilen Otomatik Zıt Anlamlılar Sözlüğü Oluşturma
Aracı (AADICT) kısaca açıklanmış ve algoritmaların gelişim süreci ayrıntılı olarak anlatılmıştır.
Türk Dil Kurumu (TDK: Türk Dil Kurumu) tarafından yayınlanan Güncel Türkçe Sözlük
verilerine AADICT uygulanarak zıt ve karşıt kelime veritabanı elde edilmiştir. Üç seviyede
işlemler uygulanarak zıt anlamlılar sözlüğü oluşturma süreci gerçekleştirilmiştir. Bu işlemlerin
bir sonucu olarak belirlenen zıt anlamlı sözcükler Kesin Zıt Anlamlı (Definite Antonym (Dn))
olarak sınıflandırılmış ve Zıt Anlamlı Sözcükler Listesi’ne (Antonym List (ALi)) konulmuştur. Kesin
Zıt Anlamlı (Dn) olarak sınıflandırılamayan bazı sözcükler Belirsiz Dosya (Ambiguity File (AF))
olarak adlandırılan ve ileride daha güvenilir zıt anlamlılar veritabanını oluşturabilmek
amacıyla denetimli yöntemlerle kontrol edilebilecekleri bir dosyada saklanmıştır.
Kesin Zıt Anlamlılar Veritabanı (Definite Antonyms Database (DADB)) olarak adlandırılan
zıt anlamlılar listesi, Güncel Türkçe Sözlük üzerine AADICT uygulanarak inşa edilmiş ve TDK
resmi internet sitesinde "Türkçe Karşıt Anlamlılar Sözlüğü" olarak yayınlanmıştır. Geliştirilen
bu sözlük, okullarda herhangi bir seviyedeki öğrencilere ve yabancı dil olarak Türkçe öğrenen
öğrencilere Türkçeyi doğru şekilde öğrenmelerine yardımcı olabilecektir. Ayrıca, bilişimsel
dilbilim alanında yapılan çalışmalarda da kullanılabilecektir.
Anahtar Kelimeler: zıt anlam, sözlük, Türkçe, dilbilim.
1 Res.Asst.Dr., Dokuz Eylul University Computer Engineering Department, e-mail:
[email protected] 2 Asst.Prof.Dr., Dokuz Eylul University Computer Engineering Department, e-mail:
[email protected], Corresponding Author 3 Asst.Prof.Dr., Dokuz Eylul University Linguistics Department, e-mail: kosaner.ozgun
@deu.edu.tr 4 Türk Dil Kurumu, e-mail: [email protected] 5 Prof.Dr., Dokuz Eylul University Computer Engineering Department, e-mail:
IJTET, Cilt 1, Sayı 2, Temmuz 2016
Çağdaş Can Birant, Özlem Aktaş, Özgün Koşaner,
Belgin Aksu, Yalçın Çebi
Uluslararası Türkçe Eğitimi ve Öğretimi Dergisi: Kuram ve Uygulama 2
Abstract
In this paper, an Automated Antonym Dictionary Generation Tool for Turkish
(AADICT) is briefly described and the development process of the algorithms is given in
details. By applying the AADICT on to the data of Contemporary Turkish Dictionary, which is
published by Turkish Linguistic Association (TDK: Türk Dil Kurumu), antonyms and opposite
words database was obtained. The antonym dictionary generation process was carried out
through three processes. As a result of these processes, the definite antonyms were classified as
Definite Antonym (Dn) and put into the Antonym List (ALi). Some words, which could not be
classified as Dn, were classified as “Ambiguity” and stored in a file called Ambiguity File (AF) to
be checked out by supervised methods to build more reliable antonym database.
The antonyms database, which is called “Definite Antonyms Database (DADB)”, for
Contemporary Turkish Dictionary was built by applying AADICT, has been currently
published as “Turkish Antonyms Dictionary” on the official web site of TDK. This dictionary
will help the students that get as lesson in the school at any level or learn Turkish as a foreign
language. Also, it may be used in the researches of computational linguistics field.
Keywords: antonym, dictionary, Turkish, linguistics.
1. Introduction
A dictionary is a collection of words (also called headwords) in a specific
language, often listed alphabetically, with definitions, etymologies, phonetics,
pronunciations, and other information (Agnes, 1999). Dictionaries are
commonly printed as the form of a book, but nowadays they can also be used as
online via the Internet.
Specialized dictionaries are sometimes found in specialized areas, such as
idioms, proverbs, synonyms, acronyms, antonyms, etc. Some concept of assets
may meet the opposite of the other words in a language. These words, which
mean opposite to other words, are called antonyms. Antonyms are opposite
pairs of words. They may have fully opposite or nearly opposite meanings,
which are used for communicating easily by expressing thoughts appropriately.
Linguists have found that there are three general categories of antonyms
(Juveland, 2012):
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I. Complementary Pairs
Complementary pairs of antonyms are perfect opposites without a
meaning of intermediate states in between them. For example; Hungry is
the complementary antonym of full, far is the complementary antonym of
near.
II. Gradable Pairs
Gradable pairs of antonyms are opposites at the extreme ends of a
continuum of states. For example; first is the gradable antonym of last.
III. Relational Opposites
Relational opposite antonyms are words with opposite meanings that are
used in similar conditions. For example teach is the relational opposite of
learn.
Antonym dictionaries have, at least, the following advantages for both
general users and researchers, especially for researchers in the field of
linguistics specifically:
Automated data extraction from a large body of text corpus,
Query clustering applications to help the search engines which use a
question-answer structure (Wen, Nie and Zhang, 2002),
Automatic indexing procedures to help to assign each word-stem to a
concept class (Salton, 1971),
Automatic machine translation studies (Edmonds, 1999),
Automatic author recognition through lexical choice (Reiter and Sripada,
2002).
Defining verbs’ conceptual structures and event types in order to provide
more complete verb frames for syntactic parser software (Chu-Ren and
Hong, 2005),
Producing more lexically cohesive texts for authors from various fields
(Donnely, 1994),
Revealing the interactional relationship between syntax and semantics
(Chief et. al., 2000),
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Helping foreign language teachers in teaching vocabulary and helping
learners of a second language in using appropriate vocabulary according
to the situation (Martin 1984).
In this paper, the Automated Antonym Dictionary Generation Tool
(AADICT) developed for Turkish has been described in details.
2. Automated Antonym Dictionary Generation Tool for Turkish
(AADICT)
2.1. The Structure of the Data Used for AADICT
The basic data source used in this study is The Contemporary Turkish
Dictionary, which includes more than 70,000 words and published by the
Turkish Linguistic Association (TDK). Antonyms are given in different forms in
this dictionary database. After taken from the Turkish Linguistic Association,
unnecessary fields and tables is removed from this database and the database
structure is simplified as given in Table 1.
Table 1. The Structure of the Source Data
Head Word Meaning
Aç (Hungry)
Yemek yemesi gereken, tok karşıtı.
(Need to eat food, opposite of satiated)
Yiyecek bulamayan
(Unable to find food)
Gözü doymaz, haris
(Eye insatiable, greedy)
Çok istekli, hevesli
(Very eager, enthusiastic)
Karnı doymamış
(Unsaturated)
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Tok (Full)
Açlığını gidermiş, doymuş, aç karşıtı
(Eliminated hunger, satiated, opposite of hungry)
Kalın ve gür (ses)
(Thick and lush (sound))
Sevgi, sevecenlik, başarı, para, mal vb. şeyleri elde etmiş ve bunlara
kavuşmuş olan
(The one that achieved and reached the love, compassion, success, money, goods,
etc.)
As given in Table 1, some words have more than one meaning, and some
meanings include both synonyms and antonyms. In general, synonyms are
located as separate words in the meaning part, but in some cases, they are
located at the end of the descriptive sentence. And, antonyms are located before
the word opposite at the end of the meaning part, but in some cases there is more
than one word which are separated by a comma before the word opposite and
complementary antonym of the main word, for example “Kara: siyah, ak, beyaz
karşıtı”. Therefore, some ambiguities occurred during the antonym extraction
process.
2.2.General Flow of AADICT
In this study, depending on both the characteristics of Turkish and the
structure of the Turkish Dictionary, an Automated Antonym Dictionary
Generation Tool (AADICT) was developed. The general workflow diagram of
the tool is given in Figure 1.
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Uluslararası Türkçe Eğitimi ve Öğretimi Dergisi: Kuram ve Uygulama 6
Antonym Parser
Turkish
Dictionary
Definite
Antonyms
Database
(DADB)
Ambiguity
Resolver
Ambiguity File
(AF)
DB Simplification
Figure 1: General Workflow Diagram.
In order to extract acronyms from the database and put them in an order,
an acronym parser should be used. By using the simplified dictionary database,
including head words and meanings of each word, the “Antonym Parser (AP)”
module determines the antonyms and the words causing ambiguities.
The words which are definitely classified as “antonyms” are stored in
another database for further processes and the words or phrases classified as
“ambiguities” are stored in a file for resolving process which is carried out by
supervised techniques.
In the dictionary database simplification step, the meanings of the headwords
were taken one by one from different tables in the current Turkish dictionary database,
and a new simplified database was generated. After generating the simplified
database, the acronym parsing process is applied by “Antonym Parser (AP)” module.
The flowchart of the AP module is given in Figure 2.
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Turkish
Dictionary
Read headword
(mainword)
Read Meaning of
mainword
Last head word
Store antonyms in AL
into Definite Antonyms
Database (DADB)
Get a PO
Last meaning
Chain Cross Check Process
no
no
yes
yes
Last PD
yes
Cross Check Process
no
Antonym Parsing Process
A
B
C
D
E
Get a PD
F
Last POno
yes
Get a D
WR
Store D in
Antonym List (AL)
G
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Figure 2: General workflow diagram of “Antonym Parser” module
2.3.Description of AADICT
In the first step of the process, which can be defined as antonym parsing,
meanings of head words are taken one by one and examined (Figure 3).
Get the words between commas and Dn
and indicate as PDn
Meaning includes
the word “opposite”
Get POs
yes
no
A
B
W
Get last word between comma and the
word “opposite”, indicate as Dn
R
Figure 3: Workflow Diagram of “Antonym Parsing Process”
The head word, the antonym of which is to be searched, is denoted as
“main word”. Each meaning of the main word is parsed and controlled if it is a
whole sentence, a single word or a sequence of single words separated by
commas (“,”) with the word “opposite of” located end of the sentence. A word
in the meaning of the main word is accepted as a “Possible (POn)” antonym,
when it is located in the meaning part alone, or a single word between commas,
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with the word “opposite of” located at end of the sentence. The word located
just before the word “opposite” is accepted as the antonym of the main word
and classified as “definite (Dn)” antonym, and stored in the “Antonym List (AL)”.
For the data given in Table 1, the words “tok (full)” was classified as
definite antonym (D) and stored in “Antonym List (AL)” for the main word “aç
(hungry)”; the word “doymuş (satiated)” was classified as possible synonym
(PO), the word “aç (hungry)” was classified as Definite antonym (D) and stored
in AL for the main word “tok” in the “Antonym Parsing Process” module.
After the antonym parsing process, all POn antonyms are cross-checked
with the head words in the dictionary (Figure 4). If it is found as a head word in
the dictionary then it is classified as “Pre-Definite (PDn)” acronym to be
processed in the next module, otherwise it is discarded.
PO
is a head word
Classify as “Pre-Definite
Antonym(PDn)”
yes
Ignore PO
C
D
no
Figure 4: Workflow Diagram of “Cross Check Process”
After a POn antonym is classified as PDn antonym, the antonym chain
cross check process is applied (Figure 5). In this process, meaning of each PDn is
checked as if it includes the main word before the word “opposite” located end
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of the meaning. If so, it is classified as Dn, otherwise it is stored in Ambiguity
File to be processed later.
Read Meaning of a PD
Meaning includes
“opposite of main word” yes
no Classify PD as D
F
E
G
Figure 5: Workflow Diagram of “Chain Cross Check Process”
The chain cross check process continues until no more words are found
to be classified as Dn. In some cases, the entire dictionary is required to be
checked to find the antonyms of the main word.
The notations, which are used in “Antonym Parser” module, are simply
given as follows.
𝑃𝑂(𝑤) = { 𝑊𝑗 , 𝑗 = 1 … 𝑚,
𝑚: 𝑠𝑖𝑛𝑔𝑙𝑒 𝑤𝑜𝑟𝑑𝑠 𝑠𝑒𝑝𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑐𝑜𝑚𝑚𝑎𝑠 𝑏𝑒𝑓𝑜𝑟𝑒 𝑤𝑜𝑟𝑑 opposite
𝑖𝑛 𝑡ℎ𝑒 𝑚𝑒𝑎𝑛𝑖𝑛𝑔𝑠 𝑜𝑓 "𝑚𝑎𝑖𝑛 𝑤𝑜𝑟𝑑"
𝑃𝐷(𝑤) = { 𝑊𝑖 , 𝑖 = 1 … 𝑛,𝑛: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑂𝑠, 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 "Cross Check Process" 𝑚𝑜𝑑𝑢𝑙𝑒
𝐷(𝑤) = { 𝑃𝐷𝑎, 𝑎 = 1 … 𝑛,𝑛: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝐷𝑠 , 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑 𝑖𝑛 " 𝐶hain Cross Checking Process"
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𝐴𝑛𝑡𝑜𝑛𝑦𝑚(𝑤) = { 𝐷𝑖(𝑤) , 𝑖 = 1 … 𝑛, 𝑛: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐷𝑠 𝑖𝑛 𝑡ℎ𝑒 𝐴𝐿
The processes given above can be explained on a sample word. The
selected main word for this process is “beyaz (white)” which has five different
meanings in Turkish. The meanings of the main word, the Pre-Definite and
Possible Antonyms obtained during the processes are given in Table 2.
Table 2. The Meanings of Main Words, Pre-definite and Possible Antonyms
Head Word Meaning
Beyaz (White)
Ak, kara, siyah karşıtı.
(Hoar, opposite of dark, black.)
Bu renkte olan.
(Things which are in this colour.)
Beyaz ırktan olan kimse.
(A person belonging to white race.)
Baskıda normal karalıkta görünen harf çeşidi.
(A kind of letter visible in normal darkness at press.)
Beyaz zehir.
(White poison/heroin.)
Ak (Hoar)
Kar, süt vb.nin rengi, beyaz, kara ve siyah karşıtı
(The color of the snow, milk, etc., white, the opposite of dark and black)
Bu renkte olan
(Things which are in this colour.)
Beyaz leke
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(White stain)
Temiz
(Clean)
Dürüst
(Honest)
Sıkıntısız, rahat
(Untroubled, comfortable)
Siyah (Black)
Kara, ak, beyaz karşıtı
(Dark, the opposite of hoar and White)
Bu renkte olan
(Things which are in this colour.)
Baskıda başka harflerden daha kalın görünen harf türü
(Bold letters in the print)
Kara (Dark)
En koyu renk, siyah, ak, beyaz karşıtı
(The darkest colour, black, opposite of hoar, white.)
In the first step of the general process, which is called as “Antonym
Parsing Process”, the word “siyah (black)” classified as Definite Antonym (D1)
and stored in Antonym List for the main word “beyaz (white)”; the words “ak
(hoar)” and “kara (dark)” are defined as “Possible Antonym (PO1 and PO2)”
(Figure 6).
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Figure 6: Antonym Parsing Process for “beyaz” Main Word
After the “antonym parsing process” module, the “cross checking process”
module run, the words “ak (hoar)” and “kara (dark)”were checked if they exist in
the dictionary as head words. All of these words were found in the dictionary,
classified as PD1 and PD2 respectively (Figure 7).
Figure 7: Cross Checking Process for “ak (hoar)” and “kara (dark)”.
In the “Chain Cross Checking Process” step, firstly, the meanings of PD1
and PD2 were checked whether they includes the word “opposite”. In the first
meaning of the PD1,“ak (hoar)”, the word located just before the word
“opposite” was not same as the main word, so PD1 was ignored and put into
the ambiguity list to be processed later. In the first meaning of the PD2, “kara
(dark)”, the PD2 was classified as D2 and stored in Antonym List (AL) (Figure 8).
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Figure 8: Definite Antonyms for The Main Word “beyaz”.
After the definite antonyms found by AADICT, they were stored into the
“Definite Antonym Database (DADB)” in the form as shown in Table 3.
Table 3. Sample Data in DADB
Head Word Antonyms
beyaz siyah
ak kara
ince iri / kaba / kalın /pes
kaba ince
2.4.Ambiguities and Solving Methods
Like synonyms, the antonyms of the head word are written in the full
meaning between commas. Therefore, during antonym search process, some
ambiguities are appeared such as in the “beyaz (white)” headword which has
five different meanings as given in Table 1.
In the first meaning of the headword “beyaz”, the definition is given as
“Ak, kara, siyah karşıtı”. The words between commas, “ak” and “kara” are
accepted as POs. Also, after applying the method mentioned above onto these
words, both of them are determined as Pre-Definite antonyms (PDn) of the
word “beyaz”.
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However, even “ak” is synonym of “beyaz”, “kara” is antonym. Since the
word “ak” is located in the meaning part of “kara” and is determined as its pre-
definite antonym, this problem cannot be solved during cross check process.
Meanings, which include such problems (ambiguities), has also an additional
phrase as “ ….. karşıtı (opposite of …)” at the end. In most cases, this problem
should be taken into consideration by taking the preceding word just before
that phrase. The PDs or POs, which have such ambiguities and cannot be solved
by the AADICT system, are stored in a file named as “Ambiguity File (AF)” for
further supervised resolving processes.
The supervised process is carried out by the experts in Turkish Linguistic
Association (TDK) and Dokuz Eylul University Linguistics Department. After
the verification process, the antonyms, which are denoted as D of any head
word, are stored into the DADB.
Conclusion
The antonyms dictionaries help the students that get as lesson in the
school at any level or learn Turkish as a foreign language. They are also used by
the researchers work in Natural Language Processing field. The studies on the
computerized analysis of Turkish, such as n-gram analysis, dependency
analysis of words, automated machine translation, automatic author detection,
etc., were begun in the 1990s and till now an antonym dictionary has not been
created. Therefore, with the collaboration of Turkish Linguistic Association
(TDK: Türk Dil Kurumu), an Automated Antonym Dictionary Generation Tool
for Turkish (AADICT) was developed.
In this study, AADICT is described in general. The data used throughout
this study was taken from Contemporary Turkish Dictionary, which is
published by TDK. By using this dictionary data, an antonym database for
Turkish has been developed and has currently been published by TDK in the
official web site at www.tdk.org.tr (Turkish Language Association, 2012)
(Figure 9).
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Figure 9: Antonyms of The Word “ince” in the “Online Antonyms Dictionary of
TDK”
Since the dictionary database was not suitable and included unnecessary
data, a simplification and filtering process was carried out by removing
unnecessary fields and tables. Although the antonyms in the dictionary were
correctly determined by AADICT, in some cases, depending on both the nature
of Turkish and structure of the dictionary, some words were mis-determined.
For example, some words have more than one meaning, and some meanings
include both synonyms and antonyms in the data source, resulting ambiguities
in antonyms extraction process. In general, antonyms are located at the end of
the meaning list, before the word “opposite” such as “aç: yemek yemesi gereken,
tok karşıtı”, but in some cases, they are located at the end of the descriptive
sentence after the “,” (comma) mark such as “beyaz: ak, kara, siyah karşıtı”.
However, the antonyms are located in the same way may cause ambiguity, such
as in the meaning of “beyaz”. In order to generate a reliable antonym dictionary
and overcome some ambiguities, supervised methods are required. For the
antonym dictionary, all ambiguities were overhauled and finalized by the
experts in Turkish Linguistic Association (TDK) and Dokuz Eylül University
Linguistics Department.
IJTET, Cilt 1, Sayı 2, Temmuz 2016
Çağdaş Can Birant, Özlem Aktaş, Özgün Koşaner,
Belgin Aksu, Yalçın Çebi
Uluslararası Türkçe Eğitimi ve Öğretimi Dergisi: Kuram ve Uygulama 17
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