Resources for Turkish dependency parsing: introducing the BOUN Treebank and the BoAT annotation tool

Türk, Utku; Atmaca, Furkan; Özateş, Şaziye Betül; Berk, Gözde; Bedir, Seyyit Talha; Köksal, Abdullatif; Başaran, Balkız Öztürk; Güngör, Tunga; Özgür, Arzucan

doi:10.1007/s10579-021-09558-0

Cited by 17 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This treebank contains linguistic examples from a grammar book to increase the coverage of different morphosyntactic constructions while minimizing the annotation effort. Two relatively larger and more recent dependency treebanks are the Boğaziçi University (BOUN) treebank (Türk et al, 2021) and the Turkish web treebank (TWT, Kayadelen et al, 2020). The BOUN treebank annotates a selection of sentences from the TNC (Aksan et al, 2012, see Section 2.1) covering a number of different text types.…”

Section: Treebankmentioning

confidence: 99%

Resources for Turkish Natural Language Processing: A critical survey

Çöltekin¹,

Doğruöz²,

Çetinoğlu³

2022

Preprint

View full text Add to dashboard Cite

This paper presents a comprehensive survey of corpora and lexical resources available for Turkish. We review a broad range of resources, focusing on the ones that are publicly available. In addition to providing information about the available linguistic resources, we present a set of recommendations, and identify gaps in the data available for conducting research and building applications in Turkish Linguistics and Natural Language Processing.

show abstract

Section: Treebankmentioning

confidence: 99%

Resources for Turkish Natural Language Processing: A critical survey

Çöltekin¹,

Doğruöz²,

Çetinoğlu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It uses a validation script developed by ud to display errors. boat-v1 was used to create the boun Treebank [2,8] -a manually annotated Turkish dependency treebank comprising close to 10 thousand sentences.…”

Section: Boat-v1mentioning

confidence: 99%

“…Annotation tools with drag-drop and mouse-based interfaces, although appealing, are not well suited for agglutinative languages as they require alternating among input modalities, disrupting the flow. boat-v1 [2] is an annotation tool that was developed to support dependency annotation of morphologically rich languages (MRLs) to produce treebanks compliant with the ud framework [1]. The experience during the use of it revealed several points of improvement for such annotation tools.…”

Section: Introductionmentioning

confidence: 99%

BoAT v2 -- A Web-Based Dependency Annotation Tool with Focus on Agglutinative Languages

Akkurt¹,

Marşan²,

Uskudarli³

2022

Preprint

View full text Add to dashboard Cite

The value of quality treebanks is steadily increasing due to the crucial role they play in the development of natural language processing tools. The creation of such treebanks is enormously labor-intensive and time-consuming. Especially when the size of treebanks is considered, tools that support the annotation process are essential. Various annotation tools have been proposed, however, they are often not suitable for agglutinative languages such as Turkish. boat-v1 was developed for annotating dependency relations and was subsequently used to create the manually annotated boun Treebank (UD_Turkish-BOUN). In this work, we report on the design and implementation of a dependency annotation tool (boat-v2) based on the experiences gained from the use of boat-v1, which revealed several opportunities for improvement. boat-v2 is a multi-user and web-based dependency annotation tool that is designed with a focus on the annotator user experience to yield valid annotations. The main objectives of the tool are to: (1) support creating valid and consistent annotations with increased speed, (2) significantly improve the user experience of the annotator, (3) support collaboration among annotators, and ( 4) provide an open-source and easily deployable web-based annotation tool with a flexible application programming interface (API) to benefit the scientific community. This paper discusses the requirements elicitation, design, and implementation of boat-v2 along with examples.

show abstract

“…We evaluated the Stanford's neural parser as the baseline system and the proposed hybrid parser with rule-based and morphology-based enhancement methods on the IMST-UD Treebank, 5 the Turkish PUD Treebank [51], and the BOUN Treebank [52] which is a newly introduced treebank for Turkish. In all of the experiments, the default set of parameters are used for the deep network that produces the parse trees.…”

Section: Experiments a Experimental Settingsmentioning

confidence: 99%

“…The training set of the IMST-UD Treebank consists of 3,685 sentences. To be able to observe the effect of the gradual increase of the training data more accurately, we additionally used the BOUN Treebank [52], a newly introduced Turkish treebank annotated in UD style. Being the largest dependency treebank in Turkish, the BOUN Treebank includes a total of 9,761 manually annotated sentences (7,803 training, 979 development, and 979 test sentences) from various topics including biographical texts, national newspapers, instructional texts, popular culture articles, and essays.…”

Section: The Effect Of Training Data Sizementioning

confidence: 99%

A Hybrid Deep Dependency Parsing Approach Enhanced With Rules and Morphology: A Case Study for Turkish

Özateş¹,

Özgür²,

Güngör³

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is not high-resource and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on three different Turkish treebanks and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.INDEX TERMS Dependency parsing, computational linguistics, recurrent neural networks.

show abstract

Resources for Turkish dependency parsing: introducing the BOUN Treebank and the BoAT annotation tool

Cited by 17 publications

References 28 publications

Resources for Turkish Natural Language Processing: A critical survey

Resources for Turkish Natural Language Processing: A critical survey

BoAT v2 -- A Web-Based Dependency Annotation Tool with Focus on Agglutinative Languages

A Hybrid Deep Dependency Parsing Approach Enhanced With Rules and Morphology: A Case Study for Turkish

Contact Info

Product

Resources

About