---
title: "Text-level Discourse Dependency Parsing — Li 2014"
description: "Earlier systems built RST-style constituency trees, and the authors identify three weaknesses in that… 28 references, cited by 61 papers here."
authors: ["Sujian Li", "Liang Wang", "Ziqiang Cao", "Wenjie Li"]
year: 2014
venue: "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P14-1003/"
doi: "10.3115/v1/p14-1003"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2014-text-level-discourse-dependency-parsing/"
---
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# Text-level Discourse Dependency Parsing

**Authors:** [Sujian Li](https://md.research.elamur.ai/free/en/authors/sujian-li/), [Liang Wang](https://md.research.elamur.ai/free/en/authors/liang-wang/), [Ziqiang Cao](https://md.research.elamur.ai/free/en/authors/ziqiang-cao/) and [Wenjie Li](https://md.research.elamur.ai/free/en/authors/wenjie-li/) · **Year:** 2014 · **Venue:** [Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2014](https://md.research.elamur.ai/free/en/venues/acl/2014/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P14-1003/) · [PDF](https://aclanthology.org/P14-1003.pdf) · [DOI 10.3115/v1/p14-1003](https://doi.org/10.3115/v1/p14-1003) · [OpenAlex](https://openalex.org/W2158211888)

**Citations:** 94 citations (OpenAlex, 2026-10-14) · [61](https://md.research.elamur.ai/free/en/papers/li-2014-text-level-discourse-dependency-parsing/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses text-level discourse parsing, which means organising a whole document into one discourse structure. Earlier systems built RST-style constituency trees, and the authors identify three weaknesses in that approach: production rules for inner spans are hard to define, units at different levels need different features, and approximate search can get stuck in local optima. Their alternative links elementary discourse units (EDUs) directly through head–dependent arcs, with an artificial root unit e0, and drops intermediate spans entirely.

To get training data, they convert RST-DT into a discourse dependency treebank. Nuclei serve as heads, and n-ary relations are binarised. Parsing is graph-based and arc-factored. The Eisner algorithm produces projective trees and the MST algorithm produces non-projective ones. Arc weights are learned with MIRA, a large-margin method, using six feature types.

On RST-DT the parser is competitive with or better than HILDA and other systems, especially for nuclearity and relation labeling. The authors present this as the first use of dependency representations and parsers for discourse. Its main appeal is a simpler framework with lower computational cost.

## Key points
- Resource: a discourse dependency treebank converted from RST-DT. RST-DT uses 110 fine-grained relations grouped into 18 classes. Its 381 n-ary relations were binarised, with the first nucleus taken as head.
- Methods: Eisner (projective) and MST (non-projective) decoding over arc-factored scores, with weights trained by MIRA. A perceptron variant (Percep-coarse) serves as a comparison.
- Full-parser results (span / nuclearity / relation):
  - Our-coarse: 82.9 / 73.0 / 60.6
  - Our-fine: 83.4 / 73.8 / 57.8
  - HILDA-manual: 83.0 / 68.4 / 55.3
  - Human agreement: 88.1 / 77.5 / 66.0
- Relation labeling, Our-coarse vs. Feng & Hirst (2012):
  - MAFS: 0.454 vs. 0.440
  - WAFS: 0.643 vs. 0.607
  - Accuracy: 66.84% vs. 65.30%
  - Majority baseline (always Elaboration): 35.82%
- The converted treebank contains no non-projective trees, although the formal definition allows them. The authors name non-projective discourse parsing as future work.

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

## Institutions

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 100 works · 35,618 citations
- [Hong Kong Polytechnic University](https://md.research.elamur.ai/free/en/universities/hong-kong-polytechnic-university/) — University, Hong Kong, China · 34 works · 4,476 citations

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🧭 Topics (2): [🧵 Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · 394 works · [🌳 Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) · 120 works

## Cite

Cite as: Sujian Li, Liang Wang, Ziqiang Cao and Wenjie Li. 2014. Text-level Discourse Dependency Parsing. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{li2014text,
  title = {{Text-level Discourse Dependency Parsing}},
  author = {Sujian Li, Liang Wang, Ziqiang Cao and Wenjie Li},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2014},
  doi = {10.3115/v1/p14-1003},
  url = {https://doi.org/10.3115/v1/p14-1003}
}
```

- [← previous Reflections on the Penn Discourse TreeBank, Comparable Corpora, and Complementary Annotation](https://md.research.elamur.ai/free/en/papers/prasad-2014-reflections-penn-discourse-treebank-comparable/)
- [next → Semantic Frame Identification with Distributed Word Representations](https://md.research.elamur.ai/free/en/papers/hermann-2014-semantic-frame-identification-distributed-word/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/)

## References

28 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*