Olcay Taner Yıldız

Also published as: Olcay Taner Yildiz


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Building the Turkish FrameNet
Büşra Marşan | Neslihan Kara | Merve Özçelik | Bilge Nas Arıcan | Neslihan Cesur | Aslı Kuzgun | Ezgi Sanıyar | Oğuzhan Kuyrukçu | Olcay Taner Yildiz
Proceedings of the 11th Global Wordnet Conference

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.

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HisNet: A Polarity Lexicon based on WordNet for Emotion Analysis
Merve Özçelik | Bilge Nas Arıcan | Özge Bakay | Elif Sarmış | Özlem Ergelen | Nilgün Güler Bayezit | Olcay Taner Yıldız
Proceedings of the 11th Global Wordnet Conference

Dictionary-based methods in sentiment analysis have received scholarly attention recently, the most comprehensive examples of which can be found in English. However, many other languages lack polarity dictionaries, or the existing ones are small in size as in the case of SentiTurkNet, the first and only polarity dictionary in Turkish. Thus, this study aims to extend the content of SentiTurkNet by comparing the two available WordNets in Turkish, namely KeNet and TR-wordnet of BalkaNet. To this end, a current Turkish polarity dictionary has been created relying on 76,825 synsets matching KeNet, where each synset has been annotated with three polarity labels, which are positive, negative and neutral. Meanwhile, the comparison of KeNet and TR-wordnet of BalkaNet has revealed their weaknesses such as the repetition of the same senses, lack of necessary merges of the items belonging to the same synset and the presence of redundant narrower versions of synsets, which are discussed in light of their potential to the improvement of the current lexical databases of Turkish.

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Turkish WordNet KeNet
Özge Bakay | Özlem Ergelen | Elif Sarmış | Selin Yıldırım | Bilge Nas Arıcan | Atilla Kocabalcıoğlu | Merve Özçelik | Ezgi Sanıyar | Oğuzhan Kuyrukçu | Begüm Avar | Olcay Taner Yıldız
Proceedings of the 11th Global Wordnet Conference

Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through interlingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.

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Creating Domain Dependent Turkish WordNet and SentiNet
Bilge Nas Arıcan | Merve Özçelik | Deniz Baran Aslan | Elif Sarmış | Selen Parlar | Olcay Taner Yıldız
Proceedings of the 11th Global Wordnet Conference

A WordNet is a thesaurus that has a structured list of words organized depending on their meanings. WordNet represents word senses, all meanings a single lemma may have, the relations between these senses, and their definitions. Another study within the domain of Natural Language Processing is sentiment analysis. With sentiment analysis, data sets can be scored according to the emotion they contain. In the sentiment analysis we did with the data we received on the Tourism WordNet, we performed a domain-specific sentiment analysis study by annotating the data. In this paper, we propose a method to facilitate Natural Language Processing tasks such as sentiment analysis performed in specific domains via creating a specific-domain subset of an original Turkish dictionary. As the preliminary study, we have created a WordNet for the tourism domain with 14,000 words and validated it on simple tasks.

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FrameForm: An Open-source Annotation Interface for FrameNet
Büşra Marşan | Olcay Taner Yıldız
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

In this paper, we introduce FrameForm, an open-source annotation tool designed to accommodate predicate annotations based on Frame Semantics. FrameForm is a user-friendly tool for creating, annotating and maintaining computational lexicography projects like FrameNet and has been used while building the Turkish FrameNet. Responsive and open-source, FrameForm can be easily modified to answer the annotation needs of a wide range of different languages.

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From Constituency to UD-Style Dependency: Building the First Conversion Tool of Turkish
Aslı Kuzgun | Oğuz Kerem Yıldız | Neslihan Cesur | Büşra Marşan | Arife Betül Yenice | Ezgi Sanıyar | Oguzhan Kuyrukçu | Bilge Nas Arıcan | Olcay Taner Yıldız
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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.


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TRopBank: Turkish PropBank V2.0
Neslihan Kara | Deniz Baran Aslan | Büşra Marşan | Özge Bakay | Koray Ak | Olcay Taner Yıldız
Proceedings of the 12th Language Resources and Evaluation Conference

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.


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English-Turkish Parallel Semantic Annotation of Penn-Treebank
Bilge Nas Arıcan | Özge Bakay | Begüm Avar | Olcay Taner Yıldız | Özlem Ergelen
Proceedings of the 10th Global Wordnet Conference

This paper reports our efforts in constructing a sense-labeled English-Turkish parallel corpus using the traditional method of manual tagging. We tagged a pre-built parallel treebank which was translated from the Penn Treebank corpus. This approach allowed us to generate a resource combining syntactic and semantic information. We provide statistics about the corpus itself as well as information regarding its development process.

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Comparing Sense Categorization Between English PropBank and English WordNet
Özge Bakay | Begüm Avar | Olcay Taner Yıldız
Proceedings of the 10th Global Wordnet Conference

Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.

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Automatic Propbank Generation for Turkish
Koray AK | Olcay Taner Yıldız
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Semantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears a semantic role. It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering. However, creating SRL models are difficult. Even in some languages, it is infeasible to create SRL models that have predicate-argument structure due to lack of linguistic resources. In this paper, we present our method to create an automatic Turkish PropBank by exploiting parallel data from the translated sentences of English PropBank. Experiments show that our method gives promising results.

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An Open, Extendible, and Fast Turkish Morphological Analyzer
Olcay Taner Yıldız | Begüm Avar | Gökhan Ercan
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this paper, we present a two-level morphological analyzer for Turkish. The morphological analyzer consists of five main components: finite state transducer, rule engine for suffixation, lexicon, trie data structure, and LRU cache. We use Java language to implement finite state machine logic and rule engine, Xml language to describe the finite state transducer rules of the Turkish language, which makes the morphological analyzer both easily extendible and easily applicable to other languages. Empowered with the comprehensiveness of a lexicon of 54,000 bare-forms including 19,000 proper nouns, our morphological analyzer presents one of the most reliable analyzers produced so far. The analyzer is compared with Turkish morphological analyzers in the literature. By using LRU cache and a trie data structure, the system can analyze 100,000 words per second, which enables users to analyze huge corpora in a few hours.


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AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and Relatedness
Gökhan Ercan | Olcay Taner Yıldız
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.


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Constructing a Turkish-English Parallel TreeBank
Olcay Taner Yıldız | Ercan Solak | Onur Görgün | Razieh Ehsani
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)