Semantic Role Labeling (SRL) is a well-defined task where the objective is to analyze propositions expressed by the verb. In SRL, each word that bears a semantic role in the sentence has to be identified. There are different types of arguments (also called ’thematic roles’) such as Agent, Patient, Instrument, and also of adjuncts, such as Locative, Temporal, Manner, and Cause. These arguments and adjuncts represent entities participating in the event and give information about the event characteristics.
In the field of SRL, PropBank is one of the studies widely recognized by the computational linguistics communities. PropBank is the bank of propositions where predicate- argument information of the corpora is annotated, and the semantic roles or arguments that each verb can take are posited.
Each verb has a frame file, which contains arguments applicable to that verb. Frame files may include more than one roleset with respect to the senses of the given verb. In the roleset of a verb sense, argument labels Arg0 to Arg5 are described according to the meaning of the verb. For the example below, the predicate is “announce” from PropBank, Arg0 is “announcer”, Arg1 is “entity announced”, and ArgM- TMP is “time attribute”.
[ARG0 Türk Hava Yolları] [ARG1 indirimli satışlarını] [ARGM-TMP bu Pazartesi] [PREDICATE açıkladı].
[ARG0 Turkish Airlines] [PREDICATE announced] [ARG1 its discounted fares] [ARGM-TMP this Monday].
The following Table shows typical semantic role types. Only Arg0 and Arg1 indicate the same thematic roles across different verbs: Arg0 stands for the Agent or Causer and Arg1 is the Patient or Theme. The rest of the thematic roles can vary across different verbs. They can stand for Instrument, Start point, End point, Beneficiary, or Attribute. Moreover, PropBank uses ArgM’s as modifier labels indicating time, location, temporal, goal, cause etc., where the role is not specific to a single verb group; it generalizes over the entire corpus instead.
| Tag | Meaning |
|---|---|
| Arg0 | Agent or Causer |
| ArgM-EXT | Extent |
| Arg1 | Patient or Theme |
| ArgM-LOC | Locatives |
| Arg2 | Instrument, start point, end point, beneficiary, or attribute |
| ArgM-CAU | Cause |
| ArgM-MNR | Manner |
| ArgM-DIS | Discourse |
| ArgM-ADV | Adverbials |
| ArgM-DIR | Directionals |
| ArgM-PNC | Purpose |
| ArgM-TMP | Temporals |
- Collect a set of sentences to annotate.
- Each sentence in the collection must be named as xxxx.yyyyy in increasing order. For example, the first sentence to be annotated will be 0001.train, the second 0002.train, etc.
- Put the sentences in the same folder such as Turkish-Phrase.
- Build the Java project and put the generated sentence-propbank-predicate.jar and sentence-propbank-argument.jar files into another folder such as Program.
- Put Turkish-Phrase and Program folders into a parent folder.
- Open sentence-propbank-predicate.jar file.
- Wait until the data load message is displayed.
- Click Open button in the Project menu.
- Choose a file for annotation from the folder Turkish-Phrase.
- For each predicate word in the sentence, click the word, and choose PREDICATE tag for that word.
- Click one of the next buttons to go to other files.
- Open sentence-propbank-argument.jar file.
- Wait until the data load message is displayed.
- Click Open button in the Project menu.
- Choose a file for annotation from the folder Turkish-Phrase.
- For each word in the sentence, click the word, and choose correct argument tag for that word.
- Click one of the next buttons to go to other files.
After annotating sentences, you can use DataGenerator package to generate classification dataset for the Semantic Role Labeling task.
After generating the classification dataset as above, one can use the Classification package to generate machine learning models for the Semantic Role Labeling task.
You can also see Java, Python, Cython, Js, Php, Swift, or C# repository.
To check if you have compatible C++ Compiler installed,
- Open CLion IDE
- Preferences >Build,Execution,Deployment > Toolchain
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 TurkishMorphologicalAnalysis-CPP will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/SemanticRoleLabeling-CPP.git
To import projects from Git with version control:
-
Open CLion IDE , select Get From Version Control.
-
In the Import window, click URL tab and paste github URL.
-
Click open as Project.
Result: The imported project is listed in the Project Explorer view and files are loaded.
From IDE
After being done with the downloading and opening project, select Build Project option from Build menu. After compilation process, user can run SemanticRoleLabeling-CPP.
The first task in Semantic Role Labeling is detecting predicates. In order to detect the predicates of the sentence, we use autoPredicate method of the TurkishSentenceAutoPredicate class.
AnnotatedSentence sentence = ...
TurkishSentenceAutoPredicate turkishAutoPredicate = new TurkishSentenceAutoPredicate(new FramesetList());
turkishAutoPredicate.autoPredicate(sentence);
Afterwards, one has to annotate the arguments for each predicate. We use autoArgument method of the TurkishSentenceAutoArgument class for that purpose.
TurkishSentenceAutoArgument turkishAutoArgument;
turkishAutoArgument.autoArgument(sentence);
@article{tbtkelektrik400987,
journal = {Turkish Journal of Electrical Engineering and Computer Science},
issn = {1300-0632},
eissn = {1303-6203},
address = {},
publisher = {TÜBİTAK},
year = {2018},
volume = {26},
pages = {570 - 581},
doi = {},
title = {Construction of a Turkish proposition bank},
key = {cite},
author = {Ak, Koray and Toprak, Cansu and Esgel, Volkan and Yıldız, Olcay Taner}
}
- First install conan.
pip install conan
Instructions are given in the following page:
https://docs.conan.io/2/installation.html
- Add conan remote 'ozyegin' with IP: 104.247.163.162 with the following command:
conan remote add ozyegin http://104.247.163.162:8081/artifactory/api/conan/conan-local --insert
- Use the comman conan list to check for installed packages. Probably there are no installed packages.
conan list
- Put the correct dependencies in the requires part
requires = ["math/1.0.0", "classification/1.0.0"]
- Default settings are:
settings = "os", "compiler", "build_type", "arch"
options = {"shared": [True, False], "fPIC": [True, False]}
default_options = {"shared": True, "fPIC": True}
exports_sources = "src/*", "Test/*"
def layout(self):
cmake_layout(self, src_folder="src")
def generate(self):
tc = CMakeToolchain(self)
tc.generate()
deps = CMakeDeps(self)
deps.generate()
def build(self):
cmake = CMake(self)
cmake.configure()
cmake.build()
def package(self):
copy(conanfile=self, keep_path=False, src=join(self.source_folder), dst=join(self.package_folder, "include"), pattern="*.h")
copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "lib"), pattern="*.a")
copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "lib"), pattern="*.so")
copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "lib"), pattern="*.dylib")
copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "bin"), pattern="*.dll")
def package_info(self):
self.cpp_info.libs = ["ComputationalGraph"]
- Set the C++ standard with compiler flags.
set(CMAKE_CXX_STANDARD 20)
set(CMAKE_CXX_FLAGS "-O3")
- Dependent packages should be given with find_package.
find_package(util_c REQUIRED)
find_package(data_structure_c REQUIRED)
- For library part, use add_library and target_link_libraries commands. Use m library for math linker in Linux.
add_library(Math src/Distribution.cpp src/Distribution.h src/DiscreteDistribution.cpp src/DiscreteDistribution.h src/Vector.cpp src/Vector.h src/Eigenvector.cpp src/Eigenvector.h src/Matrix.cpp src/Matrix.h src/Tensor.cpp src/Tensor.h)
target_link_libraries(Math util_c::util_c data_structure_c::data_structure_c m)
- For executable tests, use add_executable and target_link_libraries commands. Use m library for math linker in Linux.
add_executable(DiscreteDistributionTest src/Distribution.cpp src/Distribution.h src/DiscreteDistribution.cpp src/DiscreteDistribution.h src/Vector.cpp src/Vector.h src/Eigenvector.cpp src/Eigenvector.h src/Matrix.cpp src/Matrix.h src/Tensor.cpp src/Tensor.h Test/DiscreteDistributionTest.cpp)
target_link_libraries(DiscreteDistributionTest util_c::util_c data_structure_c::data_structure_c m)
- Add data files to the cmake-build-debug folder.
- If needed, comparator operators == and < should be implemented for map and set data structures.
bool operator==(const Word &anotherWord) const{
return (name == anotherWord.name);
}
bool operator<(const Word &anotherWord) const{
return (name < anotherWord.name);
}
- Do not forget to comment each function.
/**
* A constructor of Word class which gets a String name as an input and assigns to the name variable.
*
* @param _name String input.
*/
Word::Word(const string &_name) {
- Function names should follow caml case.
int Word::charCount() const
- Write getter and setter methods.
string Word::getName() const
void Word::setName(const string &_name)
- Use catch.hpp for testing purposes. Add
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do this in one cpp file
line in only one of the test files. Add
#include "catch.hpp"
line in all test files. Example test file is given below:
TEST_CASE("DictionaryTest") {
TxtDictionary lowerCaseDictionary = TxtDictionary("lowercase.txt", "turkish_misspellings.txt");
TxtDictionary mixedCaseDictionary = TxtDictionary("mixedcase.txt", "turkish_misspellings.txt");
TxtDictionary dictionary = TxtDictionary();
SECTION("testSize"){
REQUIRE(29 == lowerCaseDictionary.size());
REQUIRE(58 == mixedCaseDictionary.size());
REQUIRE(62113 == dictionary.size());
}
SECTION("testGetWord"){
for (int i = 0; i < dictionary.size(); i++){
REQUIRE_FALSE(nullptr == dictionary.getWord(i));
}
}
SECTION("testLongestWordSize"){
REQUIRE(1 == lowerCaseDictionary.longestWordSize());
REQUIRE(1 == mixedCaseDictionary.longestWordSize());
REQUIRE(21 == dictionary.longestWordSize());
}
- Enumerated types should be declared with enum class.
enum class Pos {
ADJECTIVE,
NOUN,
VERB,
ADVERB,
- Every header file should start with
#ifndef MATH_DISTRIBUTION_H
#define MATH_DISTRIBUTION_H
and end with
#endif //MATH_DISTRIBUTION_H
- Do not forget to use const expression for parameters, if they will not be changed in the function.
void Word::setName(const string &_name);
- Do not forget to use const expression for methods, which do not modify any class attribute. Also use [[dodiscard]]
[[nodiscard]] bool isPunctuation() const;
- Use xmlparser package for parsing xml files.
auto* doc = new XmlDocument("test.xml");
doc->parse();
XmlElement* root = doc->getFirstChild();
XmlElement* firstChild = root->getFirstChild();
- Data structures: Use map for hash map, unordered_map for linked hash map, vector for array list, unordered_set for hash set