This resource allows for matching of Turkish words or expressions with their corresponding entries within the Turkish dictionary, the Turkish PropBank TRopBank, morphological analysis, named entity recognition, word senses from Turkish WordNet KeNet, shallow parsing, and universal dependency relation.
The structure of a sample annotated word is as follows:
{turkish=Gelir}
{morphologicalAnalysis=gelir+NOUN+A3SG+PNON+NOM}
{metaMorphemes=gelir}
{semantics=TUR10-0289950}
{namedEntity=NONE}
{propbank=ARG0$TUR10-0798130}
{shallowParse=ÖZNE}
{universalDependency=10$NSUBJ}
As is self-explanatory, 'turkish' tag shows the original Turkish word; 'morphologicalAnalysis' tag shows the correct morphological parse of that word; 'semantics' tag shows the ID of the correct sense of that word; 'namedEntity' tag shows the named entity tag of that word; 'shallowParse' tag shows the semantic role of that word; 'universalDependency' tag shows the index of the head word and the universal dependency for this word; 'propbank' tag shows the semantic role of that word for the verb synset id (frame id in the frame file) which is also given in that tag.
You can also see Python, Cython, C++, C, Swift, Js, or C# repository.
- Java Development Kit 8 or higher, Open JDK or Oracle JDK
- Maven
- Git
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 or OpenJDK
To check if you have Maven installed, use the following command:
mvn --version
To install Maven, you can follow the instructions here.
Install the latest version of Git.
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 <your-fork-git-link>
A directory called AnnotatedSentence will be created. Or you can use below link for exploring the code:
git clone https://github.com/olcaytaner/AnnotatedSentence.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
AnnotatedSentence/pom.xmlfile - Select open as project option
- Couple of seconds, dependencies with Maven will be downloaded.
From IDE
After being done with the downloading and Maven indexing, select Build Project option from Build menu. After compilation process, user can run AnnotatedSentence.
From Console
Go to AnnotatedSentence directory and compile with
mvn compile
From IDE
Use package of 'Lifecycle' from maven window on the right and from AnnotatedSentence root module.
From Console
Use below line to generate jar file:
mvn install
<dependency>
<groupId>io.github.starlangsoftware</groupId>
<artifactId>AnnotatedSentence</artifactId>
<version>1.0.54</version>
</dependency>
To load the annotated corpus:
AnnotatedCorpus(File folder, String pattern)
a = AnnotatedCorpus(new File("/Turkish-Phrase"), ".train")
AnnotatedCorpus(File folder)
a = AnnotatedCorpus(new File("/Turkish-Phrase"))
To access all the sentences in a AnnotatedCorpus:
for (int i = 0; i < a.sentenceCount(); i++){
AnnotatedSentence annotatedSentence = (AnnotatedSentence) a.getSentence(i);
....
}
To access all the words in a AnnotatedSentence:
for (int j = 0; j < annotatedSentence.wordCount(); j++){
AnnotatedWord annotatedWord = (AnnotatedWord) annotatedSentence.getWord(j);
...
}
An annotated word is kept in AnnotatedWord class. To access the morphological analysis of the annotated word:
MorphologicalParse getParse()
Meaning of the annotated word:
String getSemantic()
NER annotation of the annotated word:
NamedEntityType getNamedEntityType()
Shallow parse tag of the annotated word (e.g., subject, indirect object):
String getShallowParse()
Dependency annotation of the annotated word:
UniversalDependencyRelation getUniversalDependency()
@INPROCEEDINGS{8374369,
author={O. T. {Yıldız} and K. {Ak} and G. {Ercan} and O. {Topsakal} and C. {Asmazoğlu}},
booktitle={2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP)},
title={A multilayer annotated corpus for Turkish},
year={2018},
volume={},
number={},
pages={1-6},
doi={10.1109/ICNLSP.2018.8374369}}