---
title: "A Novel Discourse Parser Based on Support — duVerle 2009"
description: "The paper addresses automatic discourse parsing in the Rhetorical Structure Theory (RST) framework."
authors: ["David duVerle", "Helmut Prendinger"]
year: 2009
venue: "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP"
source: "https://aclanthology.org/P09-1075/"
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/duverle-2009-novel-discourse-parser-based-support/"
---
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# A Novel Discourse Parser Based on Support Vector Machine Classification

**Authors:** David duVerle, [Helmut Prendinger](https://md.research.elamur.ai/free/en/authors/helmut-prendinger/) · **Year:** 2009 · **Venue:** Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP · **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/P09-1075/) · [PDF](https://aclanthology.org/P09-1075.pdf) · [OpenAlex](https://openalex.org/W2158570207)

**Citations:** 100 citations (openalex-snapshot · 2026-10-14) · [35](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses automatic discourse parsing in the Rhetorical Structure Theory (RST) framework. It concentrates on the harder step: building a labeled RST tree over text that has already been split into elementary discourse units (edus). The authors use two Support Vector Machine classifiers:
- **S**, a binary classifier that decides whether two neighbouring spans should be joined.
- **L**, a multi-class classifier that assigns the rhetorical relation together with nuclearity.

Both are trained on the RST Discourse Treebank (RST-DT), drawing on a broad collection of surface-level features that capture lexical, syntactic and structural information. The syntactic features come from lexicalized Penn Treebank parses. A greedy bottom-up procedure repeatedly merges the best-scoring adjacent pair, so parsing runs in linear time.

When results are normalised by each study's human agreement, the parser scores higher than the earlier text-level systems of Marcu and LeThanh et al. It also degrades much less when given automatic SPADE segmentation instead of gold edus. This shows that a fast, feature-driven supervised parser can approach human-level RST annotation, which makes real-time applications such as text-to-dialogue generation practical.

## Key points
- The label space has 41 classes: the 18 grouped RST-DT relations combined with their valid nuclearity options. The feature space contains roughly 10^5 features, and n-ary relations are binarized.
- Evaluation used the 41-file RST-DT test set and the 52 doubly-annotated files, with the remaining 340–350 documents used for training. Each setup was run with both gold and SPADE segmentation.
- On LeThanh et al.'s 21-document test set, the system reached F-scores of 72.8 (structure), 58.1 (nuclearity) and 48.1 (relations). LeThanh et al. scored 53.7, 47.1 and 39.9 on the same set.
- Relative to human agreement, the system achieves 83.0%, 75.6% and 73.9% for structure, nuclearity and relations, compared with 73.9%, 71.8% and 70.1% for LeThanh et al.
- Switching from gold to automatic segmentation lowers structure and relation F-scores by about 12–13%, compared with 46% and 67% for Marcu's parser. The full parser was later released online as HILDA.

*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: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cited by

Cited by

35 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 22 not yet.

- [Discourse parsing for multi-party chat dialogues](https://md.research.elamur.ai/free/en/papers/afantenos-2015-discourse-parsing-multi-party-chat/)
- [PDTB Discourse Parsing as a Tagging Task: The Two Taggers Approach](https://md.research.elamur.ai/free/en/papers/biran-2015-pdtb-discourse-parsing-as-tagging/)
- [Text-level Discourse Parsing with Rich Linguistic Features](https://md.research.elamur.ai/free/en/papers/feng-2012-text-level-discourse-parsing-rich/)
- [HILDA: A Discourse Parser Using Support Vector Machine Classification](https://md.research.elamur.ai/free/en/papers/hernault-2010-hilda-discourse-parser-support-vector/)
- [A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension](https://md.research.elamur.ai/free/en/papers/hernault-2010-semi-supervised-approach-improve-classification/)
- [CODRA: A Novel Discriminative Framework for Rhetorical Analysis](https://md.research.elamur.ai/free/en/papers/joty-2015-codra-novel-discriminative-framework-rhetorical/)
- [A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://md.research.elamur.ai/free/en/papers/kobayashi-2022-simple-strong-baseline-end-end/)
- [Recursive Deep Models for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/li-2014-recursive-deep-models-discourse-parsing/)
- [Multilingual Annotation and Disambiguation of Discourse Connectives for Machine Translation](https://md.research.elamur.ai/free/en/papers/meyer-2011-multilingual-annotation-disambiguation-discourse-connectives/)
- [Integer Linear Programming for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/perret-2016-integer-linear-programming-discourse-parsing/)
- [Modelling Discourse Relations for Arabic](https://md.research.elamur.ai/free/en/papers/saif-2011-modelling-discourse-relations-arabic/)
- [The CoNLL-2015 Shared Task on Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/xue-2015-conll-2015-shared-task-shallow/)
- [Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities](https://md.research.elamur.ai/free/en/papers/zhou-2011-unsupervised-discovery-discourse-relations-eliminating/)

## Cite

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

```bibtex
@inproceedings{duverle2009novel,
  title = {{A Novel Discourse Parser Based on Support Vector Machine Classification}},
  author = {duVerle, David and Prendinger, Helmut},
  booktitle = {Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP},
  year = {2009},
  doi = {10.3115/1690219.1690239},
  url = {https://doi.org/10.3115/1690219.1690239}
}
```

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