A CCG bank for T urkish: From Dependency to CCG (2023)

Abstract: In this paper, we present the building of a CCGbank for Turkish by using standardised dependency corpora. We automatically induce Combinatory Categorial Grammar (CCG) categories for each word token in the Turkish dependency corpora. The CCG induction algorithm we present here is based on the dependency relations that are defined in the latest release of the Universal Dependencies (UD) framework. We aim for an algorithm that can easily be used in all the Turkish treebanks that are annotated in this framework. Therefore, we employ a lexicalist approach in order to make full use of the dependency relations while creating a semantically transparent corpus. We present the treebanks we employed in this study as well as their annotation framework. We introduce the structure of the algorithm we used along with the specific issues that are different from previous studies. Lastly, we show how the results change with this lexical approach in CCGbank for Turkish compared to the previous CCGbank studies in Turkish.

## introduction semantic parsing is a vital tool for natural language processing (nlp) studies. automated inquiry systems, chat-box tools, search engines, and all sorts of other nlp applications make use of semantic information. the semantic information, however, is not encoded in the dependency or phrase treebanks directly. the syntactic relations in these frameworks do not follow from the semantic types of tokens such as the argument structure of predicates. therefore, there is a trend in converting these dependency annotated treebanks into a semantically transparent framework, which is ccg. ccg creates a categorical lexicon where each token is assigned a lexical category according to how it combines with the other tokens in a given sentence. this approach increases parsing scores compared to dependency parsing studies @xcite @xcite . however, the ccg approach requires a bigger corpus for the machines to learn each lexical type. there are languages that have adequate dependency corpora such as english, hindi, and italian. consequently, there are ccg induction studies over the dependency corpora of these languages @xcite @xcite . turkish, on the other hand, did not have a big dependency annotated corpus. there was only the metu-sabancı treebank @xcite . the first ccgbank conversion studies in turkish was conducted with a smaller corpus and therefore some rare categories were not repeated enough. today, bigger dependency corpora are available in turkish under the ud framework. these corpora are not only valuable because they provide a bigger parsing tool, but also they are annotated according to a universal annotation scheme that can be used for parallel annotation studies. today all the turkish dependency corpora are standardized in order to be a part of the ud framework. in this study, we aimed to employ the ud annotation framework to induce a ccgbank for turkish that can be used in all the turkish annotated corpora in the ud that consists of 671k tokens. in contrast to the previous ccg studies in turkish, we used a lexical approach in ccg instead of a morphemic approach. this is because the syntactic relations are defined based on lexemes and not morphemes in the ud framework for turkish and employing this framework has several advantages explained above. however, the ud standards keep improving for all languages and it might become morphemic in the later releases. then, the algorithm we provided here can be adapted to those changes in the ud framework and turn into a morphemic approach. this paper consists of 6 sections. first section introduces the study and our motivations for this study and it continues with an introduction to the ccg. the second section provides information about the ccgbank studies in different approaches and languages. in section 3, we introduce the dependency treebanks we used in this study. after this, we explain the algorithm we used to convert this treebank into a ccgbank in section 4. the last two sections are devoted to present the statistics from the resulting ccgbank corpus and to conclude our study. ## the input corpora the dependency treebanks that we induced a ccgbank corpus are turkish version of the penn treebank @xcite , framenet, kenet, atis, and tourism. these treebanks employ the annotation framework provided by the universal dependencies (ud). all of our input corpora are manually annotated according to the ud annotation framework (de marneffe et al., 2021) and they are in the conll format. all of them are available online at ud foot_0 , and free of license. the dependency annotation employed in the treebanks we used is illustrated in figure 2 . the relations build constituents. the morpho-syntactic layer of the treebank consists of pos tag, and morphological information. the morphological features change according to the word category. for instance, definiteness is only defined for nouns, tense/aspect/modality are only defined for verbs, degree information is only defined for adjectives, and so on. as illustrated in figure 2 , the relations used in this treebank differ from the previous treebanks used in the earlier works of the ccgbank studies in turkish. @xcite employed the onu geçirecek oya sahip degiller noun verb noun verb aux he-acc pass-fut vote-dat own not-pl obj acl obj root aux "they don't have the votes to pass it." metu-sabancı treebank corpus (atalay et al., @xcite . for instance, in figure 2 , the adjectival modifier with the verbal root is labelled as acl, and this signifies that it is a clausal adjective, otherwise, an adjectival modifier of an np would be labeled as an amod. the morphemes are encoded in the morpho-syntactic layer, however, they are not labelled as a separate token in the surface dependency structure 2 . the morphological analysis of these corpora is processed by a semi-automatic morphological analyzer @xcite . this semi-automatic approach increased the accuracy of the analysis. the analyzer provides the possible analyses of a word token to the annotator and the annotator selects the correct one considering the context of the sentence. this way, a consistent and contextually accurate morphological analysis was achieved. semantic and dependency annotation is performed manually. ## the ccg algorithm the ccg label assignment is carried out by an algorithm that makes use of the pos information of the word tokens, the head/complement relationship between the tokens, and the dependency label between the tokens. the algorithm starts from the left edge of the sentence, sees the pos tag of the first token, and then finds where that token is connected. together with these, the relationship between the two tokens defines the ccg label of the token. once one token is defined, the algorithm continues to the next token. however, ccg assignment algorithm does not end in one iteration because of the complex ccg categories that contain x. if the token being processed, or the head word of a constituent has an x in its rule, then it means that token is not identified yet. therefore, these tokens are left for the following iterations. this process repeats itself until all the categories are defined. the algorithm is not morphemic as the previous turkish ccgbank studies @xcite . one reason for that is the dependency annotation structure of the treebank we employed. the ud turkish framework offers a detailed and consistent annotation scheme, however, it does not separate the morphemes as @xcite did in her dissertation. therefore, the input corpus does not include separate tokens. however, as the annotation scheme that was used in the turkish penn treebank accounts for cases like ellipsis which other dependency annotation frameworks fail to cover, we employed the lexical approach as the previous studies on the penn treebank induction did @xcite . even though they were phrase treebanks, the dependency annotation scheme we employed offers similar kind of information. our motivation in applying lexical approach is to make use of the universal and standard annotation scheme in our algorithm. ## results the results follow the dependency based nature of the algorithm. the bigger categories reflect the more complicated dependency relations that need to combine with each other in a ccgbank. this reflects the direct relationship between the ud-style dependency and ccg categories. the frequency of these complex categories show that they do not pose a problem for learning. one thing to notice in another thing there are 630 different categories in this treebank. this number is only a hundred above the previous studies held in turkish even though this corpus is 60 times bigger than the previous works. the imst corpus used in @xcite had 60k words while the total word count of our corpus has 516k words. we think that this shows that a lexical approach that can be applied to all dependency treebanks of turkish results in a quite convenient ccgbank corpus. ## conclusion in this study, we presented the process of inducing a ccgbank for turkish from an existing dependency treebank. we employed a transparent algorithm that can be applied to all the turkish treebanks in the ud framework without any adjustment. we introduced the dependency treebanks used in this study along with their annotation framework. we stated the consequences of a direct induction from dependency structures to the ccg approach through certain phenomenon that was also argued in the previous literature. we also showed the similarities and differences between our algorithm and the previous studies conducted in turkish for ccgbank induction. this approach already results in a consistent and parsable ccg corpus. however, the turkish annotation scheme in the ud framework becomes more morphemic in each release and we believe the adaption to the future releases of the ud annotations can easily turn our lexeme based algorithm to a morphemic one without any complication. we hope this corpus to be useful in the upcoming turkish semantic parsing studies.