From Constituency to UD -Style Dependency: Building the First Conversion Tool of T urkish (2021)

Abstract: This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original PennTreebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.

## introduction there are two types of annotated treebanks used in natural language processing (nlp) systems: constituency treebanks and dependency treebanks. they are used to represent syntactic relations, argument structures, and other hierarchical relations. constituency treebanks display groups of phrases as trees and dependency treebanks marks headdependent relations for each item. today, dependency parsers are expected to adapt to new data from a variety of genres. the universal dependencies (ud) project @xcite provides convenient corpora for these parsers. however, this kind of an adaptation requires a huge amount of data that is not suitable for manual annotation. thus, the existent constituency treebank corpora are being automatically converted into dependency treebanks as the need for dependency treebanks increased in order to train dependency parsers @xcite ; @xcite ; @xcite . constituency to dependency conversion requires a big corpus and a coherent annotation framework. ud helps to provide a framework for annotated treebanks to be used in different languages. there have been several attempts made to represent turkish language in this project and to create a common framework in turkish language treebank studies @xcite ; @xcite ; @xcite ) @xcite . however, turkish language has flexible word order as a consequence of its agglutinative morphology. such features complicates the annotation scheme for turkish and renders the language harder to parse while giving rise to problems such as non-projectivity. in their work, @xcite edited the imst treebank @xcite according to the ud framework considering the needs of the turkish language but its corpus does not provide a parallel treebank in terms of a cross-linguistic attribution to the literature and even this refinement could not help the imst treebank to adapt the new versions of the ud framework. in spite of all the attempts and refinements which have been mentioned above, turkish still lags behind compared to similar languages in the area. for instance, a dependency conversion tool is yet to be developed for turkish while there are studies present for similar languages such as hungarian @xcite . we have applied our conversion algorithms to the biggest turkish constituency treebank which is the turkish counterpart of the original penn treebank corpus @xcite , the first annotated treebank of english that set the gold standard for other treebanks. there have been several studies done for the parallel constituency treebank creation in turkish. @xcite developed an automatic translation process to translate trees from the penn treebank. they created a tool that learned from the 5000 manually translated sentences and offered possible translations. @xcite fine tuned this work by deleting empty projections, and rearranging the two word constructions that were appearing in one node due to the cross-linguistic consequences. in their study, @xcite translated sentences using a tree-based approach to deal with the long distance dependencies that are not present in the corresponding english sentences. these long distance dependencies occur as a result of the difference between english and turkish in having a fixed word order and a flexible word order respectively. the turkish penn constituency treebank used in this conversion study, however, is distinct from the previous parallel turkish constituency treebank studies as the most recent one is translated and annotated manually. it consists of 10.000 annotated sentences foot_1 translated from the original ver-sion of the penn treebank @xcite . in order to make the translations suitable for the turkish language, a team of linguists deleted, changed some tags from the original penn treebank, and also they introduced new tags that are necessary for a better syntactic representation of the turkish language. we preferred using the turkish penn treebank in our conversion study as it offered an accurate and less complicated constituency treebank that is necessary for the demands of our conversion tool. our main objective in this study is to introduce the first turkish constituency to dependency converter by using the parallel turkish constituency treebank introduced above. another contribution of this work to the literature comes from its cross-linguistic nature. since most of the conversion studies used the penn treebank corpus @xcite ; @xcite ; @xcite , our choice of employing a translated corpus results in a turkish-english parallel constituency to dependency conversion study. all the previous converter studies employed a rule-based approach. @xcite differs from these studies in that they included extra labels to include semantic information and to handle syntactic phenomena such as topicalization, clefting, gapping, and so on. they put the semantic annotations in the penn treebank in use to achieve a more semantically rich dependency treebank. however, as the labels were more complicated, the employment of semantic information decreased the parsing accuracy. the stanford dependency approach, on the other hand, did not use these function labels that were manually annotated over the penn treebank corpus. in their study, @xcite 2 conversion process ## conclusion overall, this project presents a remarkable contribution to ud studies in turkish, in that it intro-duces the first constituency to the dependency conversion tool. furthermore, the performance comparison which was provided shows that machine learning algorithms are able to achieve better accuracy scores compared to rule-based approaches. we hope that this tool will prove to be useful in future studies on turkish dependency corpora as well. thanks to the rapid transformation from phrase structure trees, it will allow for the creation of more dependency data, paving the way for better turkish dependency parsers. moreover, as a result of this conversion, a corpus of 10,000 new sentences has been added to the turkish dependency corpora, constituting one of the largest dependency corpora in turkish. besides, considering that the penn treebank constituency trees have previously been transformed into dependency trees with the help of many conversion tools such as the stanford conversion tool @xcite and clearnlp @xcite the output we provide represents an impeccable overview of comparative structures of english and turkish ud trees, thus offering a cross-linguistic perspective.