---
title: "Who cites BinLin: A Simple Method of Dependency Tree…"
description: "The papers of the Elamur Research library that cite «BinLin: A Simple Method of Dependency Tree Linearization», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/puzikov-2018-binlin-simple-method-dependency-tree/cited-by/"
---
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# Who cites «BinLin: A Simple Method of Dependency Tree Linearization»

6 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/puzikov-2018-binlin-simple-method-dependency-tree/)

1. [Head-First Linearization with Tree-Structured Representation](https://md.research.elamur.ai/free/en/papers/yu-2019-head-first-linearization-tree-structured/) *— our card · 2019 · Proceedings of the 12th International Conference on Natural Language Generation*
2. [LORIA / Lorraine University at Multilingual Surface Realisation 2019](https://aclanthology.org/D19-6312/) *— 2019 · Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)*
3. [Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language](https://aclanthology.org/P19-2011/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop*
4. [Surface Realisation Using Full Delexicalisation](https://aclanthology.org/D19-1305/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
5. [Weighted posets: Learning surface order from dependency trees](https://aclanthology.org/W19-7807/) *— 2019 · Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)*
6. [Synthetic Data Made to Order: The Case of Parsing](https://aclanthology.org/D18-1163/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
