Dependency Parsing in a Morphological rich language, T amil (2021)

Abstract: Dependency parsing is the process of analysing the grammatical structure of a sentence based on the dependencies between the words in a sentence. The annotation of dependency parsing is done using different formalisms at word-level namely Universal Dependencies and chunk-level namely AnnaCorra. Though dependency parsing is deeply dealt in languages such as English, Czech etc the same cannot be adopted for the morphologically rich and agglutinative languages. In this paper, we discuss the development of a dependency parser for Tamil, a South Dravidian language. The different characteristics of the language make this task a challenging task. Tamil, a morphologically rich and agglutinative language, has copula drop, accusative and genitive case drop and pro-drop. Coordinative constructions are introduced by affixation of morpheme ‘um’. Embedded clausal structures are common in relative participle and complementizer clauses. In this paper, we have discussed our approach to handle some of these challenges. We have used Malt parser, a supervised learning- approach based implementation. We have obtained an accuracy of 79.27% for Unlabelled Attachment Score, 73.64% for Labelled Attachment Score and 68.82% for Labelled Accuracy.

## introduction dependency parsing is the process of analysing the grammatical structure of a sentence based on the dependencies between the words in a sentence. it gives the necessary information for various sophisticated nlp tasks such as information extraction, machine translation, and detailed sentiment analysis etc. extensive research in development of dependency treebanks and dependency parsers are done in languages such as english, czech, french, german, arabic, turkish etc. the annotation of dependency relations is done using different formalisms at word-level namely universal dependencies and inter and intra chunk-level namely annacorra. in indian languages, particularly, in hindi, telugu and bengali, there are a good number of publications on dependency parser development compared to other indian languages. lack of dependency relation annotated corpus in most of the indian languages is the prime reason. in this paper, we have described dependency relation annotation task and the development of dependency parser for tamil using a data-driven approach. the paper is structured as follows. in section 2, we have discussed the previous attempts in the development of dependency parsers in various indian languages. a brief introduction on the characteristics of tamil language is given in section 3. in section 4, we describe the annotation of dependency relations in tamil sentences. section 5 has the details of our approach for the development of tamil dependency parser. this is followed by a section containing the details of the experiment, results and discussion. the paper concludes with the conclusion section. icon tool contest on dependency parsing during 2009 and 2010 boosted the dependency parser research in languages namely hindi, telugu and bengali. different dependency parsers using grammar-driven approaches, malt parser and mst parser and hybrid approaches were developed. the detailed report on the tool contents is available in @xcite and @xcite . there are substantial works in these languages using the data released in these tool contests. few of the notable work in these languages are as follows. kesidi r. @xcite has presented a twostage constraint-based approach to telugu dependency parser, where they perform a selective identification and resolution of the dependency relation at different stages. the ranking strategy using s-constraints is used to get the best parse. praveen @xcite has presented a work on development of telugu annotated corpus and their experiment in developing telugu dependency parser using malt parser and mst parser. the annotated treebank had 2424 sentences. @xcite has presented a simple and effective dependency parser for telugu using bert model built using telugu wikipedia corpus. they have attempted to use contextual vector representations instead of handcrafted features using linguistic information such as part-of-speech and morphology. naman @xcite has done a considerable study on hindi dependency parser development. he has explained two different ensembling approaches namely re-parsing algorithm and word-by-word voting algorithm in improving the malt parser. @xcite came up with a two-stage dependency parser for bengali, where in the second stage, bangla specific constraints using bangla verb frames were used. morphological features, part-of-speech, chunk and named entity information were used in both stages. in tamil, there are very few published works in dependency parser. ramaswamy l. and zabokrtsky z. (2011) attempted to build tamil dependency parser using rule-based technique and also using malt parser and mst parser. their annotation schema was partially based on prague dependency treebank (pdt). they annotated a corpus of 3000 words. they observed that the both the rule-based and corpus-based approaches performed poorly in identifying the co-ordinate constructions. sarveswaran k. and dias g. (2020) has presented a neural-based dependency parser for tamil namely tamizhiudp, developed using uuparser engine developed using styme s. et. al. (2018) for training with heterogeneous treebanks. as they had 600 tamil dependency relation annotated sentences, they experimented multilingual training. they jointly trained tamil data with hindi httb v2.6 sentences. it has 16647 sentences. they also tried training with other languages such as telugu, turkish, but they got better result with hindi data as the data size was bigger. they got 62.39% as label attachment score (las) accuracy. since telugu mtg udv2.6 had only 1328 sentences and without morphological information, it did not suit for combined training in their experiment. in the following section, we give a brief introduction on characteristics of tamil language. ## general characteristics of tamil language tamil belongs to the south dravidian family of languages. it is a verb final language and allows scrambling. it has postpositions, the genitive precedes the head noun in the genitive phrase and the complementizer follows the embedded clause. adjective, participial adjectives and free relatives precede the head noun. it is a nominativeaccusative language like the other dravidian languages. the subject of a tamil sentence is mostly nominative, although there are constructions with certain verbs that require dative subjects. tamil has person, number and gender (png) agreement. tamil is a relatively free word order language, but when it comes to noun phrases and clausal constructions it behaves as a fixed word order language. as in other languages, tamil also has optional and obligatory parts in the noun phrase. head noun is obligatory and all other constituents that precede the head noun are optional. clausal constructions are introduced by non-finite verbs. complementizer clause occurs with complementizer markers 'endru' and 'ena'. subject drop occurs in tamil. the other characteristics of tamil are copula drop, accusative drop, and genitive drop. co-ordinate constructions are also introduced with 'um' suffix. in the next section, we will discuss about the annotation of dependency relation in tamil sentences. ## tamil dependency treebank annotation as there is no publicly available tamil dependency treebank, we prepared it inhouse. in the present work of annotating tamil sentences with dependency relations, we have followed the annotation guidelines given in annacorra @xcite . it is based on modifier-modified relationship. the dependency relations are hierarchically defined as inter-chunk and intrachunk relations. the grammatical relations that are considered in the guidelines are of two types; to handle the co-ordinate sentence formed by 'um' suffix, and sentences with copula drop, prodrop, we manually introduce a null in that required slot in the sentence and mark the dependency relations. few of the example sentences are given below. s.no relation meaning 1 k1 agent / subject / doer 2 k2 theme / patient / goal 3 k3 instrument 4 k4 recipient / experiencer 5 k5 source 6 k7 spatio-temporal 7 rt purpose 8 rh cause 9 ras associative 10 k*u comparative 11 k*s (predicative) noun / adjective complements 12 r6 genitives 13 relc modification by relative clause 14 rs noun complements (appositive) 15 adv verb modifier 16 adj noun modifier panam athikariththukkollalaam thiirnthaal celuththi thokaiyai panam miintum k1 adv k1 vmod k1 vmod ex 1: panam thiirnthaal miintum money(n) complete(v+cond) again (adverb) panam celuththi thokaiyai money(n) pay(v+vbp) amount(n) athikariththukkollalaam . increase(v+finite) the sentence in example 1, has a conditional clause 'panam thiirnthaal' (if money gets empty) and a non-finite clause 'miintum panam celuththi' (by paying money again). these two clausal verbs are attached to the finite verb has vmod relation. and it is shown in fig 2 . in example 2, the sentence has co-ordination between two nouns aitharaapaaththum (hyderabad) cekantharaapaaththum (secunderabad) introduced by 'um' suffix. while annotating this sentence, a null is introduced between these two nouns and the nouns are related to the null with 'ccof' relation. the dependency tree for this sentence is given in fig 3 . ## our approach we have used malt parser for developing tamil dependency parser. malt parser is a language independent system, which can be used to train for any given language. maltparser is an implementation of inductive dependency parsing. the syntactic analysis of a sentence amounts to the derivation of dependency structure. it is possible to improve the performance of the system by optimizing the parameter of the transition system and optimizing the features used for the classifier system. maltparser implementation has nine deterministic parsing algorithms, viz, nivre arc-eager, nivre arcstandard, convington non-projective, convington projective, stack projective, stack swap-eager, stack swap-lazy, planner and 2-planner. it supports two machine learning packages, libsvm and liblinear. in the present work, we have used arc-eager transition and morphologically rich features. nivre. j (2009) has presented the importance of morphological information in developing dependency parser for morphologically rich language namely telugu. we have used libsvm classifier with the following features pos, chunk information along with the root word, case suffix, other suffixes and tam (tense-aspect-model) and png (person, number and gender) information from verbs. this information is obtained from the morphological analysis of the words. dependency relation annotated sentences were enriched with morphological information, part-of-speech, chunk and chunk-head information using a robust morphological analyser build using a paradigm approach, crfs based part-of-speech tagger and chunker and rule-based head chunk identification module. the head chunk information is required for marking the interchunk relation. as tamil sentences have copula drop and coordinate sentences with 'um' suffix, we need to re-structure these sentences by adding null in the required positions. ## experiment and results the dependency relation annotated sentences were randomly divided into 80% and 20% for training and testing purpose. both the training data and the testing data are processed with morphological analyser, pos tagger, chunker and chunk head identification module. further sentences with copula drop and sentences with 'um' co-ordinate suffix are corrected by introducing a null. this processed data is presented to malt parser in conll column format for training and testing. we have evaluated the performance of the model with standard measures namely label attachment score (las), unlabeled attachment score (uas), labeled accuracy (la) metrics. the training data had 2000 sentences and the testing data had 500 sentences. we performed a detailed analysis on the contribution of each of the features in improving the accuracy. the performance scores obtained when introducing different features are given on analysing the output, we had the following observations. k2 karaka relation is marked as k1 is a common error. it has happened in the sentences where the accusative case marker is dropped in the object noun phrase. there are instances where k1 is marked as k2. r6, which marks the possessive relation between the nouns were poorly identified. as genitive case drop is common in tamil sentences, the relation between the nouns were not captured. this led to wrong tagging of dependency relations. the parser has failed to handle embedded clause sentences, where karaka relation of the clausal verb and the main verb were wrongly attached. in these sentences, the 'vmod' relation between the clausal verb and the main verb were marked correctly. k4a-karaka relation which refers to the experiencer relation, were wrongly tagged as k4 or k1. and these relations were less frequently occurred in the corpus. the parser has not handled the multiclause sentences correctly. here the 'vmod' relations were correctly tagged but the karaka relations with clausal verb and main verb were not correctly tagged. thus, to improve the parser efficiency, we need to increase the training data. further the rules should be included to handle genitive case drop, accusative case drop and subject drop. we need to identify the sentence structures using the clausal verb and handle them separately, it will reduce the errors due to wrongly attached nps with other verbs. we should also use the postprocessing rules to improve the parser efficiency.