TRopBank: Turkish PropBank V2.0

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 2763–2772 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 2763 TRopBank: Turkish PropBank V2.0 Neslihan Kara1, Deniz Baran Aslan1, Bü¸sra Mar¸san1, Özge Bakay2, Koray Ak3, Olcay Taner Yıldız4 Starlang Yazılım Danı¸smanlık1, Bo˘gaziçi University2, Akbank3, I¸sık University4 neslihan@starlangyazilim.com, deniz@starlangyazilim.com, busra@starlangyazilim.com, ozge.bakay@boun.edu.tr, koray.ak@akbank.com, olcaytaner@isikun.edu.tr Abstract In this paper, we present and explain TRopBank “Turkish PropBank v2.0”. PropBank is a hand-annotated corpus of propositions which is used to obtain the predicate-argument information of a language. Predicate-argument information of a language can help understand semantic roles of arguments. “Turkish PropBank v2.0”, unlike PropBank v1.0, has a much more extensive list of Turkish verbs, with 17.673 verbs in total. Keywords: Propbank, Turkish PropBank, Argument Structure Annotation 1. Introduction Verbs constitute a major category in human languages, ex- pressing the critical information concerning a state or an event. However, having the grasp of the mere definition of this category is not sufficient for comprehending the mean- ing or function of a given verb within a sentence. In order to do so, another essential component of verbs must be in- troduced: the argument structure. By placing a verb within the proper grammatical context and associating it with its arguments, any verbal structure can be analyzed accurately. With PropBank, our aim is to provide this indispensable contextual information through annotating the argument structure of each verb. Thus it is evident that PropBank’s function is indispensable for processing and properly inter- preting Turkish. In addition, PropBank enhances numerous NLP applications (e.g. machine translation, information extraction, question answering and information retrieval) by adding a semantic layer to the syntax, which takes the whole structure one step closer to human language. Being the complements of a verb, arguments express gram- matical information that is classified in accordance with their syntactic and semantic roles. In a sentence like "Jack gave Jenny a present", the verb “give” has a structure which corresponds to a list of arguments. "Jack" is the sub- ject (agent), "a present" is the direct object (theme) and “Jenny” is the indirect object (recipient). Note that each verb has a different argument structure and requires a dif- ferent number of arguments in various semantic roles. With TRropBank annotations, certain liberties have been taken in order to produce a more comprehensive corpus, where non-obligatory information has also been included as argu- ments. In theoretical syntax, non-obligatory bits of infor- mation are classified as adjuncts, contrasting with obliga- tory arguments. Nonetheless, with TRropBank, the scope of the term "argument" has been kept as wide as possible in order to provide an accurate representation of thematic roles. In this paper, we present our approach in expanding Turkish PropBank. The structure of this paper is as follows: In Sec- tion 2., we review the literature in order to provide informa- tion about PropBanks created for other languages. Section 3. presents details regarding the structure of verbs in Turk- ish. Section 4. gives information on the annotation …

Content truncated to 500 words