Begüm Avar


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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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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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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.