1. Natlanco Senior Supervisor:
Modeling Natural Language Syntax and Morphology in LingBench IDETM Parser, Examples from English and Persian
Supervisor:
Prof. Dr. Frank Van Eynde
Natlanco Senior Supervisor:
Filip De Brabander
Examiner:
Prof. Dr. Geert Adriaens

2. Introduction Background Defined Project and Tasks
Developing an efficient language model
Parsing in two linguistic levels
1. Morphology
2. Syntax
Conclusion
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Peyman Nojoumian

3. What is Language Parsing?
Parsing: In its broader sense is “the identification of parts of a sentence as subject, verb, object, etc. and of words in a sentence as noun (plural), verb (past tense), etc. [5]
Assigning some structures to language components.
An intermediate step in semantic analysis of an input sentence.
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Peyman Nojoumian

4. Parsing Techniques
Originated from the Formal Language Theory by Chomsky.
Using computer algorithms and dynamic programming to analyze natural languages.
Based on different techniques: top-down, bottom-up parsing, Finite State Automata, RTN, ATN, and etc.
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Peyman Nojoumian

5. Formal Language Theory
Formal Languages
Formal Grammars
Automata (‘Recognizers’ or ‘Transducers’)
Parsers
Formal language: Natural language (infinite sentences) = finite elements (vocabularies)
Grammars (automata): Rule systems, e.g. S NP VP
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Peyman Nojoumian

6. Types of Formal Grammars
Type-0: Enumerable languages (no restrictions on the rules):
Not interesting for Natural Languages because of its random and uncertain nature. No uniform way to assign structures to sentences.
SE AB AB cde
Type-1: Context-sensitive grammar interesting for NL, decidable rules. Can assign structures to sentences.
|A| |B| / X_Y
in-possible  impossible |n| |m| / _ [labial consonants] Labialization rule in generative phonology
Type-2: Context-free grammar Left-hand side only one node S NP VP Good to show derivations. Decidable algorithms. Large number of well-defined recognition algorithms.
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Peyman Nojoumian

7. Chomsky’s hierarchy
Type-3: Finite-state grammar Regular grammars. Right-hand side contains 1 terminal node followed by 1 non-terminal node. A aB
2-Context-free
0-enumerable
1-context-sensitive
3-Regular
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Peyman Nojoumian

8. RTN & ATN
RTN: Recursive Transition Network. It contains FSA (Finite State Automata) as directed transitions of terminal or non-terminal nodes. “In an RTN, every time the machine comes to an arc labeled with a non-terminal, it treats that non-terminal as a subroutine.
It places its current location onto a stack, jumps to the non-terminal and then jumps back when that non-terminal has been parsed.” [5]
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Peyman Nojoumian

9. Simple Demonstration
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Peyman Nojoumian

10. Augmented Transition Network
An extension to RTN
Implements conditions and actions within the transitions (for unification).
Uses features
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Peyman Nojoumian

11. BTN, RTN, ATN BTN (Basic Transition Network) [finite-state grammar]
Via
RTN (Recursive Transition Network) [context-free grammar] To
ATN (Augmented Transition Network) [unrestricted rewrite system]
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Peyman Nojoumian

12. LingBench IDETM
“Integrated Development Environment”, a program user interface that allows the user to create, modify, model, parse, and manipulate a set of linguistic data.
The core parser: Mainly a combination of different parsing techniques.
Employs RTN, embeds FSA as phrases, and uses probabilities to disambiguate and conditional features to allow only well-formed transitions in compatibility with ATN.
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Peyman Nojoumian

13. Very efficient parsing. Demonstration
LingBench IDETM
Uses an enhanced 3-Dimensional graphic environment for easy, fast and efficient language modeling.
Very easy to use.
Fast and reliable.
Very efficient parsing.
Demonstration
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Peyman Nojoumian

14. Hardware and Software Requirements
A Pentium with at least 1GHz CPU speed and 256MB of RAM equipped with an advanced graphic accelerator.
Works on Windows platforms.
A light version is available to download from the Internet:
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Peyman Nojoumian

15. Defined Project and Tasks
The project was defined to be done in three main phases.
Developing language models will be easier if it is done in cycles.
1. Getting acquainted with LingBench IDETM, Spotting available corpora (lexicon and sentences), Starting the modeling by 20 simple sentences with limited lexicon, Reporting the application’s bugs.
Tuesday, February 05, 2019Tuesday, February 05, 2019
Peyman Nojoumian

16. Defined Project and Tasks
2. Developing a lexicon of 1000 words, sentence corpus with 150 sentences as the training set, Developing further rules and testing them, Reporting bugs.
3. Creating a lexicon of words and 250 sentences, Using the corpus to design rules and testing them, Reporting bugs (5 new versions).
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Peyman Nojoumian

17. Syntactic model of Persian Sentences
Modeling was started with modeling 7 phrases using 10 word classes.
At the end 16 rules (phrases) were created and a total of 28 word classes (POSs) were implemented based on the training set.
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Peyman Nojoumian

18. Sentence Model Tuesday, February 05, 2019Tuesday, February 05, 2019
Peyman Nojoumian

19. Sample of a Parsed Sentence
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Peyman Nojoumian

20. Morphological Model of Words
using 2000 word stems of simple verbs and a number of related verb affixes, a large number of our examples from Persian verb forms were recognized by the morphological grammar module.
11 phrases and 36 morphological classes designed and modeled in the morphological grammar.
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Peyman Nojoumian

21. An example of the Morphological Analyzer
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Peyman Nojoumian

22. Further Functionalities of the LingBench IDETM
Lexicon development
Three lexicons: 1)syntactic 2)morphological 3)multi-word
Defining Features and efficient probabilities
Defining variables in nodes and transitions to add more constraints.
Unicode compatibility.
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Peyman Nojoumian

23. Conclusion
It was expected that the system would be able to parse correctly at least 50% of the 200 sentences at the end of the 3rd phase.
100% parsable sentences of the corpus. Estimated to be able to parse 80% of the words and more than 50% of the sentences of a new test corpus.
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Peyman Nojoumian

24. Conclusion
Efficient, Fast and reliable parsing was made possible with an enriched lexicon featuring subcategories, word frequencies and relative word probabilities with a simple model combining features of the RTN and ATN while using probabilities as well as feature structures.
Using the model for parser module of my PhD thesis: “Designing a diacritizer for Persian”
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Peyman Nojoumian

25. References Tuesday, February 05, 2019Tuesday, February 05, 2019
1.Assi, S. Mostafa & M. Haji Abdolhosseini (2000). “Grammatical Tagging of a Persian Corpus”, International Journal of Corpus Linguistics, Vol. 5(1), PP
2.Bateni, M. (1998). “Towsife sakhtemane dasturiye zabane Farsi” (Persian Syntax). Amir Kabir Publishers: Tehran.
3.De Brabander, Filip (2003). LingBench IDETM Users Manual.
4.Garside, R. & et all eds. (1997). Corpus Annotation, Linguistic information from Computer Text Corpora. Longman.
5.Jurafsky, Daniel & J.H. Martin (2000). An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, New Jersey.
6.Kennedy, Graeme (1998). An Introduction to Corpus Linguistics, Longman.
7.Lambton Ann K. (1996) “Persian Grammar”, London: Cambridge University Press.
8.Lazard G. (1992) “A Grammar of Contemporary Persian”, California & New York: Mazda Publishers.
9.Longman Dictionary of Language Teaching & Applied Linguistics (1992). London.
10.Manning, Christopher D. & H. Schutze (2001). Foundation of statistical natural language processing. The MIT press, Cambridge
11.Nojoumian, Peyman (1999). Design and Implementation of a Computer Assisted Language Learning (CALL) System for Persian. MA Dissertation, Alame Tabatabaei University, Tehran.
12.Sag, Ivan A. & Thomas Wasow (1999). Syntactic Theory, A formal Introduction, CSLI, Stanford, USA.
13.Van Eynde, Frank and D. Gibbon Eds. (2000). Lexicon Development for Speech and Language Processing, Kluwer Academic Publishers, London.
14.Van Valin, Robert D. & Randy J. Lapolla (1997). Syntax Structure, Meaning and Function, Cambridge University Press
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Peyman Nojoumian