Building the T urkish F rame N et (2021)

Abstract: FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) is a computational lexicography project that aims to offer insight into the semantic relationships between predicate and arguments. Having uses in many NLP applications, FrameNet has proven itself as a valuable resource. The main goal of this study is laying the foundation for building a comprehensive and cohesive Turkish FrameNet that is compatible with other resources like PropBank (Kara et al., 2020) or WordNet (Bakay et al., 2019; Ehsani, 2018; Ehsani et al., 2018; Parlar et al., 2019; Bakay et al., 2020) in the Turkish language.

## introduction introduced in 1997, framenet @xcite @xcite has been developed by the international computer science institute in berkeley, california. it is a growing computational lexicography project that offers in-depth semantic information on english words and predicates. based on the theory of frame semantics by fillmore @xcite , framenet offers semantic information on predicate-argument structure in a way that is loosely similar to wordnet @xcite . in framenet, predicates and related lemmas are categorized under frames. the notion of frame here is thoroughly described in frame semantics as a schematic representation of an event, state or relationship. these semantic information packets called frames are constituted of individual lemmas (also known as lexical units) and frame elements (such as the agent, theme, instrument, duration, manner, direction etc.). frame elements can be described as semantic roles that are related to the frame. lexical units, or lemmas, are linked to a frame through a single sense. for instance, the lemma "roast" can mean to criticise harshly or to cook by exposing to dry heat foot_0 . with its latter meaning, "roast" belongs to the apply heat frame. with this study, we aimed to recreate a comprehensive framenet in turkish language following fellbaum's notions related to frame semantics theory. for this purpose, we referred to english framenet's frames and turkish wordnet's properties. in order to ensure compatibility with turkish wordnet (kenet) and turkish propbank (tropbank), we used the same lemma ids. in this paper, we present our attempt at building a turkish framenet. in section 2 titled towards a turkish framenet, we explain our motivation, methodology and processes along with the challenges we faced during this study. in section 3 we present our results and discuss these results in section 4. finally, we conclude with our suggestions regarding further study in section 5. ## results in this study, a total number of 139 frames in 8 domains were created foot_3 . 16 of these frames were created specifically for turkish while the remaining 123 are translated from english framenet. these frames include a total number of 2769 synsets (see ## discussion the aim of this study was creating a useful resource for natural language processing studies in turkish. offering 139 frames, 2561 synsets and 4080 lexical units, this study can be considered as a very satisfactory first step towards this goal. in addition, turkish framenet is created in correspondence with english framenet. rather than being a mere translation, turkish framenet employs new frames when necessary but maintains its close ties with english framenet. that is why it can be used in both turkish nlp studies and english-turkish translation applications. moreover, this close correspondence to english framenet makes it possible to introduce cross-correspondence between turkish and various other framenets that use same or similar frames as english framenet. in many other languages, nlp resources like propbank, wordnet and framenet use different identification and processing systems for their lemmas. that is why it is rather challenging to integrate them and create enhanced, state of the art nlp solutions. on the other hand, turkish prop-bank tropbank, turkish wordnet kenet and turkish framenet use the same set of lemmas. as a result, individual synsets have the same ids across all platforms. that is why it is possible and relatively easier to integrate these three resources and create cutting edge nlp tools or train highly accurate semantic annotators. such streamlined databanks and corpora offer a great value to nlp studies in low resource languages like turkish. due to being able to easily correlate, tropbank and turkish framenet can be used together to empower nlp solutions. because of its characteristic features, propbank offers syntactic information regarding the predicates while fails to capture the semantic layer. on the other hand, framenet does not offer much information about the valency of a predicate. that is why the combination of these two offer a coherent and thorough analysis for nlp applications. since the same team is behind creating kenet, tropbank and turkish framenet, these three resources share same synsets and lemmas. thus, they can be used together in the same nlp solution without spending much effort on making them compatible. ## further studies this study is the very first attempt to a turkish framenet. that is why the primary aim was laying the foundation. in order to create initial frames and include at least some portion of the most commonly used predicates in turkish, we opted for a top-down approach. in other words, we created 139 frames in 8 domains and added related lexical units into these frames. for the next step, a bottom-up approach may be more appropriate in order to extend the coverage of framenet. for this purpose, turkish wordnet kenet @xcite @xcite @xcite can provide a very useful resource. annotators can start from the terminal branches and work their way up, creating new frames and building inheritance and/or lateral relationships between frames. in this step, kenet's own hierarchy can be a guide for creating new frames. since this study consists of only 139 frames, the lateral and hierarchical (inheritance) relations between frames are significantly limited. for in-figure 2 : frame relations stance, frames within the motion domain have a strong hierarchical relationship (see figure 2 ). for instance, "kac ¸mak" (run away) lu is a member of forward motion frame and its parent frame, motion. yet some lexical units in motion domain also correspond to sports play frame (see figure 2 ). the lateral relationship between these two overlapping frames is not strictly defined. since the number of frames are relatively low at the moment, such refinements are not crucial but as the turkish framenet grows, the necessity of defining both hierarchical and lateral relationships will be indispensable. again, the relationships determined in kenet can and should play a pivotal role for such definitions for the sake of coherence. in this study, english framenet @xcite @xcite was taken as the baseline. that is why the vast majority of the frames correspond to english ones despite some necessary deviations due to the typological characteristics of turkish. in the follow-up works, the correspondence between lexical units should be built, so that a cross-language resource that can be used in various nlp applications like machine translation is created.