---
title: "A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish — Marşan 2022"
description: "The authors propose the first algorithm that converts dependency trees into constituency trees and is tailored specifically to Turkish, a… 27 references."
authors: ["Büşra Marşan", "Oğuz K. Yıldız", "Aslı Kuzgun", "Neslihan Cesur", "Arife B. Yenice", "Ezgi Sanıyar", "Oğuzhan Kuyrukçu", "Bilge N. Arıcan", "Olcay Taner Yıldız"]
year: 2022
venue: "Proceedings of the Thirteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2022.lrec-1.540/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/marsan-2022-learning-based-dependency-constituency-conversion/"
---
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# A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language

**Authors:** [Büşra Marşan](https://md.research.elamur.ai/free/en/authors/busra-marsan/), Oğuz K. Yıldız, Aslı Kuzgun, Neslihan Cesur, Arife B. Yenice, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Bilge N. Arıcan and [Olcay Taner Yıldız](https://md.research.elamur.ai/free/en/authors/olcay-taner-yildiz/) · **Year:** 2022 · **Venue:** Proceedings of the Thirteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.lrec-1.540/) · [PDF](https://aclanthology.org/2022.lrec-1.540.pdf) · [OpenAlex](https://openalex.org/W7160317071)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The authors propose the first algorithm that converts dependency trees into constituency trees and is tailored specifically to Turkish, a language with few syntactic resources. The input is the manually annotated treebank UD Turkish-Penn. It consists of Penn Treebank sentences that professional translators translated into Turkish and that linguists annotated according to the UD guidelines. The algorithm builds the tree bottom-up. First, a subtree is created for each dependency edge. The subtrees are then combined through three actions: attach the left neighbor (Left), attach the right neighbor (Right), or merge nodes into a phrasal projection (Merge). An oracle decides which action to perform. The oracle is either a set of predefined heuristics or a feature-based machine learning model enhanced with bagging. The features are drawn from dependency types, headedness, POS and morphological analysis. The trees are deliberately flat, and phrase tags are determined from morphology. Quality was evaluated against gold-standard trees manually annotated by a team of linguists. The result is more than 8000 constituency trees parallel to the UD annotation. The work establishes a baseline for Turkish. It also demonstrates a simple way to obtain training data for constituency parsers in low-resource languages.

## Key points
- The input corpus is UD Turkish-Penn. The authors deliberately chose manual annotation rather than automatically generated dependencies. In automatic annotation, the head of contentious constructions such as "genel olarak" is often assigned inconsistently.
- Morphological features and POS are provided by the open-source Turkish morphological analyzer of Yıldız et al. (2019). The bootstrap aggregating (bagging) meta-algorithm is added to the conversion model.
- The trees are flatter than in X-Bar or the Penn Treebank. Ternary branching is allowed, and there are no X' levels or empty projections. Complements and adjuncts of the same head are placed at the same level, following the strategy of Hajič et al. (1998).
- Phrases carry only basic tags such as NP, VP, PP, DP. There are no function tags like NP-SUBJ or ADV-TMP, since the predicate-argument structure is meant to be described by the Turkish PropBank (TRopBank).
- The more than 8000 resulting constituency trees are released on GitHub together with the corresponding UD dependency trees.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [🌐 Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/) · [📐 Syntax & grammar formalisms](https://md.research.elamur.ai/free/en/topics/syntax-grammar/)

## Institutions

Also on the byline: [Özyeğin University](https://ror.org/01jjhfr75).

## Cite

Cite as: Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, Neslihan Cesur, Arife B. Yenice, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Bilge N. Arıcan and Olcay Taner Yıldız. 2022. A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language. Proceedings of the Thirteenth Language Resources and Evaluation Conference.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{marsan2022learning,
  title = {{A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language}},
  author = {Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, Neslihan Cesur, Arife B. Yenice, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Bilge N. Arıcan and Olcay Taner Yıldız},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  year = {2022},
  doi = {10.63317/5cngxuqy3u9d},
  url = {https://doi.org/10.63317/5cngxuqy3u9d}
}
```

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