https://github.com/StarlangSoftware/TurkishMorphologicalAnalysis
Turkish Morphological Analysis library
https://github.com/StarlangSoftware/TurkishMorphologicalAnalysis
finite-state-machine morphological-analyser morphological-analysis morphology turkish
Last synced: 12 days ago
JSON representation
Turkish Morphological Analysis library
- Host: GitHub
- URL: https://github.com/StarlangSoftware/TurkishMorphologicalAnalysis
- Owner: StarlangSoftware
- License: gpl-3.0
- Created: 2018-05-31T15:23:06.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2026-03-17T11:03:00.000Z (3 months ago)
- Last Synced: 2026-04-07T11:32:47.454Z (3 months ago)
- Topics: finite-state-machine, morphological-analyser, morphological-analysis, morphology, turkish
- Language: Java
- Homepage:
- Size: 5.65 MB
- Stars: 19
- Watchers: 5
- Forks: 15
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-java - TurkishMorphologicalAnalysis
README
Morphological Analysis
============
## Morphology
In linguistics, the term morphology refers to the study of the internal structure of words. Each word is assumed to consist of one or more morphemes, which can be defined as the smallest linguistic unit having a particular meaning or grammatical function. One can come across morphologically simplex words, i.e. roots, as well as morphologically complex ones, such as compounds or affixed forms.
Batı-lı-laş-tır-ıl-ama-yan-lar-dan-mış-ız
west-With-Make-Caus-Pass-Neg.Abil-Nom-Pl-Abl-Evid-A3Pl
‘It appears that we are among the ones that cannot be westernized.’
The morphemes that constitute a word combine in a (more or less) strict order. Most morphologically complex words are in the ”ROOT-SUFFIX1-SUFFIX2-...” structure. Affixes have two types: (i) derivational affixes, which change the meaning and sometimes also the grammatical category of the base they are attached to, and (ii) inflectional affixes serving particular grammatical functions. In general, derivational suffixes precede inflectional ones. The order of derivational suffixes is reflected on the meaning of the derived form. For instance, consider the combination of the noun göz ‘eye’ with two derivational suffixes -lIK and -CI: Even though the same three morphemes are used, the meaning of a word like gözcülük ‘scouting’ is clearly different from that of gözlükçü ‘optician’.
## Dilbaz
Here we present a new morphological analyzer, which is (i) open: The latest version of source codes, the lexicon, and the morphotactic rule engine are all available here, (ii) extendible: One of the disadvantages of other morphological analyzers is that their lexicons are fixed or unmodifiable, which prevents to add new bare-forms to the morphological analyzer. In our morphological analyzer, the lexicon is in text form and is easily modifiable, (iii) fast: Morphological analysis is one of the core components of any NLP process. It must be very fast to handle huge corpora. Compared to other morphological analyzers, our analyzer is capable of analyzing hundreds of thousands words per second, which makes it one of the fastest Turkish morphological analyzers available.
The morphological analyzer consists of five main components, namely, a lexicon, a finite state transducer, a rule engine for suffixation, a trie data structure, and a least recently used (LRU) cache.
In this analyzer, we assume all idiosyncratic information to be encoded in the lexicon. While phonologically conditioned allomorphy will be dealt with by the transducer, other types of allomorphy, all exceptional forms to otherwise regular processes, as well as words formed through derivation (except for the few transparently compositional derivational suffixes are considered to be included in the lexicon.
In our morphological analyzer, finite state transducer is encoded in an xml file.
To overcome the irregularities and also to accelerate the search for the bareforms, we use a trie data structure in our morphological analyzer, and store all words in our lexicon in that data structure. For the regular words, we only store that word in our trie, whereas for irregular words we store both the original form and some prefix of that word.
Simple Web Interface
============
[Link 1](http://104.247.163.162/nlptoolkit/turkish-morphological-analysis.html) [Link 2](https://starlangsoftware.github.io/nlptoolkit-web-simple/turkish-morphological-analysis.html)
Video Lectures
============
[
](https://youtu.be/KxguxpbgDQc)[
](https://youtu.be/UMmA2LMkAkw)[
](https://youtu.be/dP97ovMSSfE)[
](https://youtu.be/Tgmy5tts_pY)
For Developers
============
You can also see [Python](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-Py), [Cython](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-Cy), [C++](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-CPP), [C](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-C), [Swift](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-Swift), [Js](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-Js), [Php](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-Php), or [C#](https://github.com/starlangsoftware/TurkishMorphologicalAnalysis-CS) repository.
## Requirements
* [Java Development Kit 8 or higher](#java), Open JDK or Oracle JDK
* [Maven](#maven)
* [Git](#git)
### Java
To check if you have a compatible version of Java installed, use the following command:
java -version
If you don't have a compatible version, you can download either [Oracle JDK](https://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html) or [OpenJDK](https://openjdk.java.net/install/)
### Maven
To check if you have Maven installed, use the following command:
mvn --version
To install Maven, you can follow the instructions [here](https://maven.apache.org/install.html).
### Git
Install the [latest version of Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git).
## Download Code
In order to work on code, create a fork from GitHub page.
Use Git for cloning the code to your local or below line for Ubuntu:
git clone
A directory called MorphologicalAnalysis will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/TurkishMorphologicalAnalysis.git
## Open project with IntelliJ IDEA
Steps for opening the cloned project:
* Start IDE
* Select **File | Open** from main menu
* Choose `MorphologicalAnalysis/pom.xml` file
* Select open as project option
* Couple of seconds, dependencies with Maven will be downloaded.
## Compile
**From IDE**
After being done with the downloading and Maven indexing, select **Build Project** option from **Build** menu. After compilation process, user can run Morphological Analysis.
**From Console**
Go to `MorphologicalAnalysis` directory and compile with
mvn compile
## Generating jar files
**From IDE**
Use `package` of 'Lifecycle' from maven window on the right and from `MorphologicalAnalysis` root module.
**From Console**
Use below line to generate jar file:
mvn install
## Maven Usage
io.github.starlangsoftware
MorphologicalAnalysis
1.0.62
Detailed Description
============
+ [Creating FsmMorphologicalAnalyzer](#creating-fsmmorphologicalanalyzer)
+ [Word level morphological analysis](#word-level-morphological-analysis)
+ [Sentence level morphological analysis](#sentence-level-morphological-analysis)
## Creating FsmMorphologicalAnalyzer
FsmMorphologicalAnalyzer provides Turkish morphological analysis. This class can be created as follows:
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
This generates a new `TxtDictionary` type dictionary from [`turkish_dictionary.txt`](https://github.com/olcaytaner/Dictionary/tree/master/src/main/resources) with fixed cache size 100000 and by using [`turkish_finite_state_machine.xml`](https://github.com/olcaytaner/MorphologicalAnalysis/tree/master/src/main/resources).
Creating a morphological analyzer with different cache size, dictionary or finite state machine is also possible.
* With different cache size,
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer(50000);
* Using a different dictionary,
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer("my_turkish_dictionary.txt");
* Specifying both finite state machine and dictionary,
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer("fsm.xml", "my_turkish_dictionary.txt") ;
* Giving finite state machine and cache size with creating `TxtDictionary` object,
TxtDictionary dictionary = new TxtDictionary("my_turkish_dictionary.txt", new TurkishWordComparator());
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer("fsm.xml", dictionary, 50000) ;
* With different finite state machine and creating `TxtDictionary` object,
TxtDictionary dictionary = new TxtDictionary("my_turkish_dictionary.txt", new TurkishWordComparator(), "my_turkish_misspelled.txt");
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer("fsm.xml", dictionary);
## Word level morphological analysis
For morphological analysis, `morphologicalAnalysis(String word)` method of `FsmMorphologicalAnalyzer` is used. This returns `FsmParseList` object.
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
String word = "yarına";
FsmParseList fsmParseList = fsm.morphologicalAnalysis(word);
for (int i = 0; i < fsmParseList.size(); i++){
System.out.println(fsmParseList.getFsmParse(i).transitionList();
}
Output
yar+NOUN+A3SG+P2SG+DAT
yar+NOUN+A3SG+P3SG+DAT
yarı+NOUN+A3SG+P2SG+DAT
yarın+NOUN+A3SG+PNON+DAT
From `FsmParseList`, a single `FsmParse` can be obtained as follows:
FsmParse parse = fsmParseList.getFsmParse(0);
System.out.println(parse.transitionList();
Output
yar+NOUN+A3SG+P2SG+DAT
## Sentence level morphological analysis
`morphologicalAnalysis(Sentence sentence)` method of `FsmMorphologicalAnalyzer` is used. This returns `FsmParseList[]` object.
FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
Sentence sentence = new Sentence("Yarın doktora gidecekler");
FsmParseList[] parseLists = fsm.morphologicalAnalysis(sentence);
for(int i = 0; i < parseLists.length; i++){
for(int j = 0; j < parseLists[i].size(); j++){
FsmParse parse = parseLists[i].getFsmParse(j);
System.out.println(parse.transitionList());
}
System.out.println("-----------------");
}
Output
-----------------
yar+NOUN+A3SG+P2SG+NOM
yar+NOUN+A3SG+PNON+GEN
yar+VERB+POS+IMP+A2PL
yarı+NOUN+A3SG+P2SG+NOM
yarın+NOUN+A3SG+PNON+NOM
-----------------
doktor+NOUN+A3SG+PNON+DAT
doktora+NOUN+A3SG+PNON+NOM
-----------------
git+VERB+POS+FUT+A3PL
git+VERB+POS^DB+NOUN+FUTPART+A3PL+PNON+NOM
# Cite
@inproceedings{yildiz-etal-2019-open,
title = "An Open, Extendible, and Fast {T}urkish Morphological Analyzer",
author = {Y{\i}ld{\i}z, Olcay Taner and
Avar, Beg{\"u}m and
Ercan, G{\"o}khan},
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://www.aclweb.org/anthology/R19-1156",
doi = "10.26615/978-954-452-056-4_156",
pages = "1364--1372",
}
For Contibutors
============
### Class Diagram
### pom.xml file
1. Standard setup for packaging is similar to:
```
io.github.starlangsoftware
Amr
1.0.0
jar
NlpToolkit.Amr
Abstract Meaning Representation Library
https://github.com/StarlangSoftware/Amr
io.github.starlangsoftware
https://github.com/starlangsoftware
The Apache Software License, Version 2.0
http://www.apache.org/licenses/LICENSE-2.0.txt
Olcay Taner Yildiz
olcay.yildiz@ozyegin.edu.tr
Starlang Software
http://www.starlangyazilim.com
scm:git:git://github.com/starlangsoftware/amr.git
scm:git:ssh://github.com:starlangsoftware/amr.git
http://github.com/starlangsoftware/amr/tree/master
1.8
1.8
UTF-8
```
2. Only top level dependencies should be added. Do not forget junit dependency.
```
io.github.starlangsoftware
AnnotatedSentence
1.0.78
junit
junit
4.13.1
test
```
3. Maven compiler, gpg, source, javadoc plugings should be added.
```
org.apache.maven.plugins
maven-compiler-plugin
3.6.1
1.8
1.8
org.apache.maven.plugins
maven-gpg-plugin
1.6
sign-artifacts
verify
sign
org.apache.maven.plugins
maven-source-plugin
2.2.1
attach-sources
jar-no-fork
org.apache.maven.plugins
maven-javadoc-plugin
8
3.10.0
attach-javadocs
jar
```
4. Currently publishing plugin is Sonatype.
```
org.sonatype.central
central-publishing-maven-plugin
0.8.0
true
central
true
```
5. For UI jar files use assembly plugins.
```
org.apache.maven.plugins
maven-assembly-plugin
2.2-beta-5
sentence-dependency
package
single
Amr.Annotation.TestAmrFrame
amr
jar-with-dependencies
false
```
### Resources
1. Add resources to the resources subdirectory. These will include image files (necessary for UI), data files, etc.
### Java files
1. Do not forget to comment each function.
```
/**
* Returns the value of a given layer.
* @param viewLayerType Layer for which the value questioned.
* @return The value of the given layer.
*/
public String getLayerInfo(ViewLayerType viewLayerType){
```
2. Function names should follow caml case.
```
public MorphologicalParse getParse()
```
3. Write toString methods, if necessary.
4. Use Junit for writing test classes. Use test setup if necessary.
```
public class AnnotatedSentenceTest {
AnnotatedSentence sentence0, sentence1, sentence2, sentence3, sentence4;
AnnotatedSentence sentence5, sentence6, sentence7, sentence8, sentence9;
@Before
public void setUp() throws Exception {
sentence0 = new AnnotatedSentence(new File("sentences/0000.dev"));
```