---
title: "HILDA: A Discourse Parser Using Support — Hernault 2010"
description: "HILDA (HIgh-Level Discourse Analyzer) builds Rhetorical Structure Theory (RST) trees for whole documents, not just single sentences."
authors: ["Hugo Hernault", "Helmut Prendinger", "David A. du Verle", "Mitsuru Ishizuka"]
year: 2010
venue: "Dialogue & Discourse"
source: "https://aclanthology.org/2010.dnd-1.1/"
doi: "10.5087/dad.2010.003"
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/hernault-2010-hilda-discourse-parser-support-vector/"
---
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# HILDA: A Discourse Parser Using Support Vector Machine Classification

**Authors:** [Hugo Hernault](https://md.research.elamur.ai/free/en/authors/hugo-hernault/), [Helmut Prendinger](https://md.research.elamur.ai/free/en/authors/helmut-prendinger/), David A. du Verle, [Mitsuru Ishizuka](https://md.research.elamur.ai/free/en/authors/mitsuru-ishizuka/) · **Year:** 2010 · **Venue:** Dialogue & Discourse · **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/2010.dnd-1.1/) · [PDF](https://aclanthology.org/2010.dnd-1.1.pdf) · [DOI 10.5087/dad.2010.003](https://doi.org/10.5087/dad.2010.003) · [OpenAlex](https://openalex.org/W2165887782)

**Citations:** 246 citations (openalex-snapshot · 2026-10-14) · [107](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
HILDA (HIgh-Level Discourse Analyzer) builds Rhetorical Structure Theory (RST) trees for whole documents, not just single sentences. Earlier tools either stopped at sentence boundaries, as SPADE does, or were never fully built. The authors split the job into two stages, both handled by Support Vector Machine classifiers trained on the RST Discourse Treebank (RST-DT), a set of annotated Wall Street Journal texts. The first stage segments text into elementary discourse units (EDUs). The second stage scores pairs of adjacent spans and gives each pair a relation label and a nuclearity. A greedy bottom-up procedure then keeps merging the highest-scoring pair, so tree construction runs in time linear in the length of the text. The classifiers use lexical, syntactic and structural features, including lexico-syntactic cues from parse trees. The complete pipeline reaches 78.3% of human annotator performance and improves on a state-of-the-art rule-based parser by 11.6%. Because it is both accurate and fast, it can serve near real-time applications such as question answering and dialogue generation.

## Key points
- The system has two SVM modules. The EDU segmenter reaches about 96% of human-level performance, and the relation classifier with tree builder reaches about 84%.
- Full text-level parsing reaches 78.3% of human performance and is 11.6% better than a rule-based baseline. It also outperforms SPADE, Sagae (2009) and others on tree structure and relation labeling.
- For training, RST-DT documents are aligned with Penn Treebank syntax trees. New input is parsed with the Stanford parser. The label set has 18 relations (e.g., ELABORATION, ATTRIBUTION, CONTRAST, SAME-UNIT), each with its nuclearity options.
- The main weakness is implicit relations. In one error analysis, all ten implicit CONTRAST cases were wrongly labeled, as ELABORATION (6) or JOINT (4). Planned fixes are a separate implicit-relation classifier using word-pair and WordNet similarity features, feature selection, and globally optimal tree building.
- Intended uses include the Text-to-Dialogue system and generating dialogues from monologues with the CODA corpus (700 expository dialogues).

*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

107 papers of our library cite it (of the 140673 whose bibliographies we read); 41 of them have a page here, 66 not yet.

- [Discourse parsing for multi-party chat dialogues](https://md.research.elamur.ai/free/en/papers/afantenos-2015-discourse-parsing-multi-party-chat/)
- [Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure](https://md.research.elamur.ai/free/en/papers/allen-2014-detecting-disagreement-conversations-pseudo-monologic/)
- [Where Are We in Discourse Relation Recognition?](https://md.research.elamur.ai/free/en/papers/atwell-2021-where-are-we-discourse-relation/)
- [Better Document-level Sentiment Analysis from RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/)
- [The Potsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bourgonje-2020-potsdam-commentary-corpus-2-2/)
- [Combining Natural and Artificial Examples to Improve Implicit Discourse Relation Identification](https://md.research.elamur.ai/free/en/papers/braud-2014-combining-natural-artificial-examples-improve/)
- [Multi-view and multi-task training of RST discourse parsers](https://md.research.elamur.ai/free/en/papers/braud-2016-multi-view-multi-task-training/)
- [Cross-lingual RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/braud-2017-cross-lingual-rst-discourse-parsing/)
- [The Good, the Bad, and the Disagreement: Complex ground truth in rhetorical structure analysis](https://md.research.elamur.ai/free/en/papers/das-2017-good-bad-disagreement-complex-ground/)
- [Constructing a Lexicon of English Discourse Connectives](https://md.research.elamur.ai/free/en/papers/das-2018-constructing-lexicon-english-discourse-connectives/)
- [Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints](https://md.research.elamur.ai/free/en/papers/durrett-2016-learning-based-single-document-summarization/)
- [Text-level Discourse Parsing with Rich Linguistic Features](https://md.research.elamur.ai/free/en/papers/feng-2012-text-level-discourse-parsing-rich/)
- [A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing](https://md.research.elamur.ai/free/en/papers/feng-2014-linear-time-bottom-up-discourse/)
- [Coreference for Discourse Parsing: A Neural Approach](https://md.research.elamur.ai/free/en/papers/guz-2020-coreference-discourse-parsing-neural-approach/)
- [Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining](https://md.research.elamur.ai/free/en/papers/guz-2020-unleashing-power-neural-discourse-parsers/)
- [Analyses of the Association between Discourse Relation and Sentiment Polarity with a Chinese Human-Annotated Corpus](https://md.research.elamur.ai/free/en/papers/huang-2013-analyses-association-between-discourse-relation/)
- [Representation Learning for Text-level Discourse Parsing](https://md.research.elamur.ai/free/en/papers/ji-2014-representation-learning-text-level-discourse/)
- [Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis](https://md.research.elamur.ai/free/en/papers/joty-2013-combining-intra-multi-sentential-rhetorical/)
- [Discriminative Reranking of Discourse Parses Using Tree Kernels](https://md.research.elamur.ai/free/en/papers/joty-2014-discriminative-reranking-discourse-parses-tree/)
- [CODRA: A Novel Discriminative Framework for Rhetorical Analysis](https://md.research.elamur.ai/free/en/papers/joty-2015-codra-novel-discriminative-framework-rhetorical/)
- [Top-down Discourse Parsing via Sequence Labelling](https://md.research.elamur.ai/free/en/papers/koto-2021-top-down-discourse-parsing-sequence/)
- [Recursive Deep Models for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/li-2014-recursive-deep-models-discourse-parsing/)
- [Discourse Parsing with Attention-based Hierarchical Neural Networks](https://md.research.elamur.ai/free/en/papers/li-2016-discourse-parsing-attention-based-hierarchical/)
- [A Unified Linear-Time Framework for Sentence-Level Discourse Parsing](https://md.research.elamur.ai/free/en/papers/lin-2019-unified-linear-time-framework-sentence/)
- [Learning Contextually Informed Representations for Linear-Time Discourse Parsing](https://md.research.elamur.ai/free/en/papers/liu-2017-learning-contextually-informed-representations-linear/)
- [Multilingual Neural RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/liu-2020-multilingual-neural-rst-discourse-parsing/)
- [Using Sense-labeled Discourse Connectives for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/meyer-2012-sense-labeled-discourse-connectives-statistical/)
- [How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT](https://md.research.elamur.ai/free/en/papers/morey-2017-how-much-progress-have-we/)
- [A Dependency Perspective on RST Discourse Parsing and Evaluation](https://md.research.elamur.ai/free/en/papers/morey-2018-dependency-perspective-rst-discourse-parsing/)
- [Constrained Decoding for Text-Level Discourse Parsing](https://md.research.elamur.ai/free/en/papers/muller-2012-constrained-decoding-text-level-discourse/)
- [Two Practical Rhetorical Structure Theory Parsers](https://md.research.elamur.ai/free/en/papers/surdeanu-2015-two-practical-rhetorical-structure-theory/)
- [Annotation upon Annotation: Adding Signalling Information to a Corpus of Discourse Relations](https://md.research.elamur.ai/free/en/papers/taboada-2013-annotation-upon-annotation-adding-signalling/)
- [Implicit Discourse Relation Recognition by Selecting Typical Training Examples](https://md.research.elamur.ai/free/en/papers/wang-2012-implicit-discourse-relation-recognition-selecting/)
- [A Two-Stage Parsing Method for Text-Level Discourse Analysis](https://md.research.elamur.ai/free/en/papers/wang-2017-two-stage-parsing-method-text/)
- [Dependency-based Discourse Parser for Single-Document Summarization](https://md.research.elamur.ai/free/en/papers/yoshida-2014-dependency-based-discourse-parser-single/)
- [GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection](https://md.research.elamur.ai/free/en/papers/yu-2019-gumdrop-disrpt2019-shared-task-model/)
- [RST Discourse Parsing with Second-Stage EDU-Level Pre-training](https://md.research.elamur.ai/free/en/papers/yu-2022-rst-discourse-parsing-second-stage/)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://md.research.elamur.ai/free/en/papers/zeldes-2025-erst-signaled-graph-theory-discourse/)
- [A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure](https://md.research.elamur.ai/free/en/papers/zhang-2020-top-down-neural-architecture-text/)
- [Adversarial Learning for Discourse Rhetorical Structure Parsing](https://md.research.elamur.ai/free/en/papers/zhang-2021-adversarial-learning-discourse-rhetorical-structure/)
- [Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank](https://md.research.elamur.ai/free/en/papers/zhao-2017-joint-syntacto-discourse-parsing-syntacto/)

## Cite

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

```bibtex
@inproceedings{hernault2010hilda,
  title = {{HILDA: A Discourse Parser Using Support Vector Machine Classification}},
  author = {Hernault, Hugo and Prendinger, Helmut and Verle, David A. du and Ishizuka, Mitsuru},
  booktitle = {Dialogue \& Discourse},
  year = {2010},
  doi = {10.5087/dad.2010.003},
  url = {https://doi.org/10.5087/dad.2010.003}
}
```

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- [next → Identifying the Information Structure of Scientific Abstracts: An Investigation of Three Different Schemes](https://md.research.elamur.ai/free/en/papers/guo-2010-identifying-information-structure-scientific-abstracts/)
- [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/)

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