Developing Dependency Parsers for Tamil

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Developing Dependency Parsers for Tamil
-
March 27, 2021
K. Sarveswaran (Sarves)
University of Moratuwa
& University of Jaffna
Sri Lanka.
iamsarves@gmail.com
K. Sarveswaran (iamsarves@gmail.com) Tamil Dependency Parser March 27, 2021 1 / 21

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Overview
1 Introduction
2 Background
Natural Language Grammars
Syntactic parsing
Treebanks
Universal Dependency Treebank
Dependency Parsers
Approaches for Developing parsers
3 Dependency parsing of Tamil
4 How did I develop parsers?
5 ThamizhiPOSt: Part of Speech tagger
6 ThamizhiMorph: Morphological Analyser and Generator
7 LFG-based grammar for Tamil
8 UD-based grammar for Tamil
9 Creation of Treebank
10 Conclusion

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Introduction
language processing technologies are now part of our everyday life
tech giants are investing a lot on language technologies
interests towards local language computing are increasing in recent
times
Tamil still can be considered as a low-resource language, based
publicly available on number of usable tools and resources
machine learning/deep learning approaches are growing very fast
dependency parsers are very crucial tools for syntactic analysis

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Natural language grammars
phrase structure grammar (constituency grammar / context-free
grammar / generative grammar) and dependency grammar are the
two popular grammars used to model natural languages1
there are also several derivations of these two, for instance Lexical
Functional Grammar
phrase structure grammar - good for languages like English, where the
order of words matter
dependency grammar - good for languages that are morphologically
rich and have relatively free word order1
1 Jurafsky, D. and Martin, J.H., 2008. Speech and Language Processing: An introduction to speech recognition, computational
linguistics and natural language processing. Upper Saddle River, NJ: Prentice Hall.

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Syntactic parsing
mapping a string of words to its parse tree is called syntactic parsing2
2
parse=to separate a sentence into grammatical parts - Cambridge dictionary

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Treebanks
bank of syntactically (may be also semantically) annotated sentences
(syntactically parsed sentences)
for instance:
Penn Treebank3
- a phrase structure treebank
Universal Dependency Treebank4
- a dependency treebank
3
https://catalog.ldc.upenn.edu/LDC99T42
4
https://universaldependencies.org/

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Universal Dependency (UD) Treebank
there are several schemes for annotating dependencies: Anncora5,
PDT6
Universal Dependency Treebank7 is a widely used scheme for machine
language processing
cross-linguistically consistent treebank annotation for many languages
facilitate multilingual parser development, cross-lingual learning, and
parsing research from a language typology perspective
183 treebanks in 104 languages, as of November 2020
widely used for parsing; there are shared tasks and workshops organised
annually
5 Bharati, A., Sangal, R., Sharma, D.M. and Bai, L., 2006. Anncorra: Annotating corpora guidelines for pos and chunk annotation
for indian languages. LTRC-TR31, pp.1-38.
6 Hajic, J., Vidová-Hladká, B. and Pajas, P., 2001, December. The prague dependency treebank: Annotation structure and
support. In Proceedings of the IRCS Workshop on Linguistic Databases (pp. 105-114).
7 Nivre, J., De Marneffe, M.C., Ginter, F., Goldberg, Y., Hajic, J., Manning, C.D., McDonald, R., Petrov, S., Pyysalo, S., Silveira,
N. and Tsarfaty, R., 2016, May. Universal dependencies v1: A multilingual treebank collection. In Proceedings of the Tenth
International Conference on Language Resources and Evaluation (LREC’16) (pp. 1659-1666).

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Universal Dependency Treebank
consists POS, Lemma, Morphology, and Dependency annotations
arranged in CoNLL-U format, as shown in Figure-1
scheme is amended to accommodate language change
Figure-1
Figure-2

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Dependency Parser
A software which gives syntactic parses of a given sentence based on a
dependency formalism.
Why:
useful for the development of applications for : grammar checking,
semantic interpretation, question-answer, and machine translation
useful to study the structure of languages / diachronic and synchronic
changes
Challenges:
one needs a lot of linguistic knowledge to create treebanks
time consuming, usually (gold) treebank are created by hand
there are still a lot of debates on syntax, even for English 8
ambiguities are always a problem:
attachment: Ram saw Sita [with a telescope]
coordination: old women and men
8
https://universaldependencies.org/workgroups/core.html

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Treebanks - Tamil
only one treebank is there (publicly available): Tamil PDT9
TamilPDT then also migrated to UD (called TamilTTB) in
November, 2015, using scripts.
since then no change has been done
used by several non-Tamil teams for parsing (IWPT202010)
TamilTTB has several issues:
tokenisation: for instance, words are broken inappropriately
dependency issues: for instance, datives can be a subject, oblique,
indirect object in Tamil. However, it is mostly marked as object
9
Ramasamy, L. and Žabokrtský, Z., 2011, February. Tamil dependency parsing: results using rule based and corpus based
approaches. In International Conference on Intelligent Text Processing and Computational Linguistics (pp. 82-95). Springer,
Berlin, Heidelberg.
10
https://universaldependencies.org/iwpt20/enhancements_in_treebanks.html

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Approaches for Developing parsers
rule-based approach:
need to write a lot of rules
success and the coverage is heavily depended on the lexicon
useful for (small) domain specific parsing
hybrid-approach:
create annotated data
train a computer program with annotated data
annotate more data using the trained computer program, and do this
iteratively until get a good accuracy
useful for languages like Tamil where we do not have a lot annotated
data
robust than rule-based approach
machine learning based / unsupervised learning:
research is still in its preliminary stage

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Dependency Parser for Tamil
a shallow parser for Tamil; it identifies the phrases with a f-measure
of 66.6, tool not found11
a dependency parser for Tamil; score 57.50, no data/no tools found.
It uses own specification for annotation12
a dependency parser to parse an ancient poetic text in Tamil; no
results report, no tools found13
a SVM based dependency parser; unlabelled assigned score of 76.26;
no tools found14
There is a survey paper on parsing in Tamil15
11Ariaratnam, I., Weerasinghe, A.R. and Liyanage, C., 2014, December. A shallow parser for Tamil. In 2014 14th International
Conference on Advances in ICT for Emerging Regions (ICTer) (pp. 197-203). IEEE.
12Selvam, M., Natarajan, A.M. and Thangarajan, R., 2009. Structural parsing of natural language text in Tamil Language using
dependency model. International Journal of Computer Processing of Languages, 22(02n03), pp.237-256.
13Dhanalakshmi, V., Kumar, M.A. and Murugesan, C., 2012. Dependency Parser for Tamil classical literature-Kurunthokai.
INFITT
14
Green, N., Ramasamy, L. and Žabokrtský, Z., 2012. Using an SVM ensemble system for improved Tamil dependency parsing. In
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages
(pp. 72-77).
15
Rajendran, S., 2006. Parsing in tamil: Present state of art. Language in India, 6, p.8.

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How did I develop parsers?
this is the context in which I started developing a dependency parser
for Tamil
tried two approaches to develop a parser for Tamil:
Universal Dependency parser (UD-based) using hybrid-approach
Lexical Functional Grammar based parser (LFG-based) - rule-based
approach
also developed support tools to ease the development process of
UD-based and LFG-based development:
Part of Speech (POS) tagger (ThamizhiPOSt)
Morphological analyser (ThamizhiMorph)

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Part of Speech Tagger (ThamizhiPOSt)
there are several POS-tagsets available: Universal POS (UPOS),
Amrita, Bureau of Indian Standards (BIS)
available data:
AU-KBC Ponniyin Selvan corpus16
(BIS)
Amrita tagged corpus17
(Amrita)
TDIL has a small tagged corpus for non-Indians (BIS)
TamilTTB (Universal Dependency Treebank) has around 9K tokens
(UPOS)
ThamizhiPOSt
used UPOS - this is what used in Universal Dependency
developed using machine learning approach
converted Amrita to UPOS, and trained the program
accuracy - 93.57%18
16
http://www.au-kbc.org/nlp/corpusrelease.html
17
https://www.amrita.edu/publication/tamil-pos-tagging-using-linear-programming
18Sarveswaran, K, Gihan Dias. 2020. ThamizhiUDp: A Dependency Parser for Tamil. In Proceedings of the 17th International
Conference on Natural Language Processing (ICON-2020), IIT Patna, India.

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ThamizhiMorph: Morphological Analyser and Generator
a rule-based approach, used nominal and verbal paradigms to write
rules using a Finite-State Transducer
mostly handles inflectional morphology
paradigms:
for verbal paradigms: used Graul’s paradigm19
collected verb roots from various sources, primarily from Irākavaiyaṅkār
20
conjugational forms are obtained from various sources, including from
Crea21
auxiliary forms were taken from Lehmann22
at present:
there are 3300+ base forms and 300+ conjugations for each base
generated 1.4M+ simple and 50M+ complex surface forms23
19
K. Graul,Outline of Tamil grammar. Leipzip University, 1855
20
M. Irākavaiyaṅkār,’Viaittiripu viḷakkam’ (conjugation of Tamil verbs) (in Tamil). Eighty year anniversary publication, 1958.
21
E. Annamalai and Crea Team, A handbook of Tamil Verbal Conjugations, MCNeil Technologies, 2009
22
Lehmann, Thomas. 1993.A Grammar of Modern Tamil. Pondicherry Institute of Linguistics and Culture, India.
23
https://www.kaggle.com/sarves/tamilverbs

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LFG-based grammar for Tamil
Lexical Functional Grammar a constraint-based grammar, a
generative grammar24
goal of combining linguistic sophistication with computational
implementability
primarily has a constituency and functional structures; now also
extended to capture more complex analysis, like semantics, prosody
etc.
constituency structure (c-structure) - captures surface structure, word
order etc.
functional structure (f-structure) - captures the functions, constraints,
argument structure etc.
at present:
it is developed based on 150 sentences taken from ParGram project25
and Grade-1 Tamil text book
used ThamizhiMorph to generate lexicon
available here: https://clarino.uib.no/iness/xle-web
24Kaplan, R.M. and Bresnan, J., 1981. Lexical-functional grammar: A formal system for grammatical representation. Mas-
sachusetts Institute Of Technology, Center For Cognitive Science.
25Butt, Miriam, Tracy Holloway King, Maria-Eugenia Nino, and Frederique Segond. 1999. A Grammar Writer’s Cookbook.
Stanford: CSLI Publications.

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LFG parsing - examples

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UD-based grammar for Tamil
used a hybrid approach to develop the parser
created UD annotated treebank, using ThamizhiPOSt,
ThamizhiMorph and by hand
iteratively trained the parser using machine learning approach
also tried multilingual learning, along with Telugu and Hindi
training a parser is a structured process, as below:

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Creation of Treebanks
Tamil MWTT: (Together with Prof. Prameswari, CALTS)
Tamil Modern Written Tamil Treebank, used 536 sentences from a
book called ”Grammar of Modern Tamil” - by Thoman Lehmann
Manually (mostly) annotated dependency information
available in UD repository26; work in progress
Tamil ThamizhiTB:
annotated 1300 sentences taken from online sources (some what
balanced, taken from different type of sources), used hybrid approach
(Human + Machine)
different syntactical constructions are considered
26
https://github.com/UniversalDependencies/UD_Tamil-MWTT/tree/master

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Performance
at present:
have a parser, ThamizhiUDp, with the accuracy of 79%
covers simple structures, except questions
available through ThamizhiLIP
Also tried, multilingual training with Hindi and Telugu. Multilingual
learning is a technique used when there are less data.
Dataset LAS (F1 score)
Hindi27
(1500 sentences) 76.74
Telugu28
(1050 sentences) 75.73
27
https://github.com/UniversalDependencies/UD_Hindi-HDTB/tree/master
28
https://github.com/UniversalDependencies/UD_Telugu-MTG/tree/master

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Finally:
initial and usable versions of POS tagger, Morphological
analyser/generator, and Dependency parsers are available publicly
rule-based LFG parser and machine learning based UD parsers are
useful devices for linguistic and computational analysis of our
languages
need more data to improve these tools
need a lot more linguistic help
everything open source for others to build upon; please make use of
them
conducting a workshop on UD treebank annotation on 8-10 April,
2021.
Thank you.
K. Sarveswaran (Sarves)
iamsarves@gmail.com

Developing Dependency Parsers for Tamil

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