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’.
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.
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To check if you have a compatible version of Node.js installed, use the following command:
node -v
You can find the latest version of Node.js here.
Install the latest version of Git.
npm install nlptoolkit-morphologicalanalysis
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 util will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/morphologicalanalysis-js.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
SemanticRoleLabeling-Jsfile - Select open as project option
- Couple of seconds, dependencies will be downloaded.
- Creating FsmMorphologicalAnalyzer
- Word level morphological analysis
- Sentence level morphological analysis
FsmMorphologicalAnalyzer provides Turkish morphological analysis. This class can be created as follows:
let fsm = new FsmMorphologicalAnalyzer();
This generates a new TxtDictionary type dictionary from turkish_dictionary.txt with fixed cache size 100000 and by using turkish_finite_state_machine.xml.
Creating a morphological analyzer with different cache size, dictionary or finite state machine is also possible.
-
With different cache size,
let fsm = new FsmMorphologicalAnalyzer(50000); -
Using a different dictionary,
let fsm = new FsmMorphologicalAnalyzer("my_turkish_dictionary.txt"); -
Specifying both finite state machine and dictionary,
let fsm = new FsmMorphologicalAnalyzer("fsm.xml", "my_turkish_dictionary.txt") ; -
Giving finite state machine and cache size with creating
TxtDictionaryobject,let dictionary = new TxtDictionary("my_turkish_dictionary.txt", WordComparator.TURKISH); let fsm = new FsmMorphologicalAnalyzer("fsm.xml", dictionary, 50000) ; -
With different finite state machine and creating
TxtDictionaryobject,let dictionary = new TxtDictionary("my_turkish_dictionary.txt", WordComparator.TURKISH, "my_turkish_misspelled.txt"); let fsm = new FsmMorphologicalAnalyzer("fsm.xml", dictionary);
For morphological analysis, MorphologicalAnalysis(String word) method of FsmMorphologicalAnalyzer is used. This returns FsmParseList object.
let fsm = new FsmMorphologicalAnalyzer();
let word = "yarına";
let fsmParseList = fsm.morphologicalAnalysis(word);
for (let i = 0; i < fsmParseList.size(); i++){
console.log(fsmParseList.getFsmParse(i).getTransitionList();
}
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:
let parse = fsmParseList.getFsmParse(0);
console.log(parse.getTransitionList();
Output
yar+NOUN+A3SG+P2SG+DAT
morphologicalAnalysis(Sentence sentence) method of FsmMorphologicalAnalyzer is used. This returns FsmParseList[] object.
let fsm = new FsmMorphologicalAnalyzer();
let sentence = new Sentence("Yarın doktora gidecekler");
let parseLists = fsm.morphologicalAnalysis(sentence);
for(let i = 0; i < parseLists.length; i++){
for(let j = 0; j < parseLists[i].size(); j++){
let parse = parseLists[i].getFsmParse(j);
console.log(parse.getTransitionList());
}
console.log("-----------------");
}
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
@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",
}
- main and types are important when this package will be imported.
"main": "dist/index.js",
"types": "dist/index.d.ts",
- Dependencies should be maximum (not only direct but also indirect references should also be given), everything directly in the code should be given here.
"dependencies": {
"nlptoolkit-corpus": "^1.0.12",
"nlptoolkit-dictionary": "^1.0.14",
"nlptoolkit-morphologicalanalysis": "^1.0.19",
"nlptoolkit-xmlparser": "^1.0.7"
}
- Compiler flags currently includes nodeNext for importing.
"compilerOptions": {
"outDir": "dist",
"module": "nodeNext",
"sourceMap": true,
"noImplicitAny": true,
"removeComments": false,
"declaration": true,
},
- tests, node_modules and dist should be excluded.
"exclude": [
"tests",
"node_modules",
"dist"
]
- Should include all ts classes.
export * from "./CategoryType"
export * from "./InterlingualDependencyType"
export * from "./InterlingualRelation"
export * from "./Literal"
- Add data files to the project folder. Subprojects should include all data files of the parent projects.
- Classes should be defined as exported.
export class JCN extends ICSimilarity{
- Do not forget to comment each function.
/**
* Computes JCN wordnet similarity metric between two synsets.
* @param synSet1 First synset
* @param synSet2 Second synset
* @return JCN wordnet similarity metric between two synsets
*/
computeSimilarity(synSet1: SynSet, synSet2: SynSet): number {
- Function names should follow caml case.
setSynSetId(synSetId: string){
- Write getter and setter methods.
getRelation(index: number): Relation{
setName(name: string){
- Use standard javascript test style.
describe('SimilarityPathTest', function() {
describe('SimilarityPathTest', function() {
it('testComputeSimilarity', function() {
let turkish = new WordNet();
let similarityPath = new SimilarityPath(turkish);
assert.strictEqual(32.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0656390"), turkish.getSynSetWithId("TUR10-0600460")));
assert.strictEqual(13.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0412120"), turkish.getSynSetWithId("TUR10-0755370")));
assert.strictEqual(13.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0195110"), turkish.getSynSetWithId("TUR10-0822980")));
});
});
});
- Enumerated types should be declared with enum.
export enum CategoryType {
MATHEMATICS, SPORT, MUSIC, SLANG, BOTANIC,
PLURAL, MARINE, HISTORY, THEOLOGY, ZOOLOGY,
METAPHOR, PSYCHOLOGY, ASTRONOMY, GEOGRAPHY, GRAMMAR,
MILITARY, PHYSICS, PHILOSOPHY, MEDICAL, THEATER,
ECONOMY, LAW, ANATOMY, GEOMETRY, BUSINESS,
PEDAGOGY, TECHNOLOGY, LOGIC, LITERATURE, CINEMA,
TELEVISION, ARCHITECTURE, TECHNICAL, SOCIOLOGY, BIOLOGY,
CHEMISTRY, GEOLOGY, INFORMATICS, PHYSIOLOGY, METEOROLOGY,
MINERALOGY
}
- If there are multiple constructors for a class, define them as constructor1, constructor2, ..., then from the original constructor call these methods.
constructor1(symbol: any){
constructor2(symbol: any, multipleFile: MultipleFile) {
constructor(symbol: any, multipleFile: MultipleFile = undefined) {
if (multipleFile == undefined){
this.constructor1(symbol);
} else {
this.constructor2(symbol, multipleFile);
}
}
- Importing should be done via import method with referencing the node-modules.
import {Corpus} from "nlptoolkit-corpus/dist/Corpus";
import {Sentence} from "nlptoolkit-corpus/dist/Sentence";
- Use xmlparser package for parsing xml files.
var doc = new XmlDocument("test.xml")
doc.parse()
let root = doc.getFirstChild()
let firstChild = root.getFirstChild()