---
title: "An effective Discourse Parser that uses Rich — Subba 2009"
description: "The paper addresses discourse parsing: building a tree over a text's segments and labeling each internal node with a rhetorical relation."
authors: ["Rajen Subba", "Barbara Di Eugenio"]
year: 2009
venue: "Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/N09-1064/"
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/subba-2009-effective-discourse-parser-that-uses/"
---
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# An effective Discourse Parser that uses Rich Linguistic Information

**Authors:** [Rajen Subba](https://md.research.elamur.ai/free/en/authors/rajen-subba/), [Barbara Di Eugenio](https://md.research.elamur.ai/free/en/authors/barbara-di-eugenio/) · **Year:** 2009 · **Venue:** Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the 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/N09-1064/) · [PDF](https://aclanthology.org/N09-1064.pdf) · [OpenAlex](https://openalex.org/W2069684104)

**Citations:** 116 citations (openalex-snapshot · 2026-10-14) · [47](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses discourse parsing: building a tree over a text's segments and labeling each internal node with a rhetorical relation. Most earlier parsers used only shallow lexical and syntactic signals. Subba and Di Eugenio test whether deeper linguistic knowledge helps. They work with a corpus of home-repair how-to instructions, manually split into EDUs and annotated with 26 informational relations. They also add VerbNet-based compositional semantics, derived largely automatically with the LCFLEX parser and a CoreLex noun lexicon.

Relations are classified with Inductive Logic Programming (the Aleph system). The background knowledge includes:
- words and part-of-speech tags,
- WordNet hypernymy and meronymy,
- discourse cues,
- segment similarity,
- semantic predicates,
- the internal structure of larger segments.

This relational learner significantly beats Decision Trees, RIPPER and Naive Bayes, and it produces readable first-order rules. The classifier drives a modified shift-reduce parser, which can attach new segments anywhere on the right frontier of the tree. The parser clearly outperforms a right-branching majority-class baseline. Adding semantics gives a statistically significant gain.

## Key points
- **Corpus:** 176 instructional documents with 5744 EDUs and 53,250 words. Full semantic representations were obtained for 3257 EDUs (56.7%).
- **Relation annotation:** 5172 relations were labeled using 26 types grouped into 5 classes. The most frequent is preparation:act (24.46%). Inter-annotator agreement was κ = 0.66, rising to 0.78 when preparation:act and step1:step2 are merged. Only 43% of relations carry an explicit cue.
- **Sentence-level parsing (relation F-score):** SR-ILP with semantics reached 63.06%, compared with 58.58% without semantics (p = 0.006) and 34.32% for the baseline.
- **Document-level parsing (relation F-score):** performance falls to 35.44%, against 22.46% for the baseline. The authors attribute the drop to errors made at lower levels, which propagate upward, and they argue for a less greedy parsing strategy.
- **Evaluation setup:** 151 documents were used for training and 25 for testing. Spans, nuclearity and relations were scored with Marcu's (2000) method.

*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

47 papers of our library cite it (of the 140884 whose bibliographies we read); 20 of them have a page here, 27 not yet.

- [Discourse parsing for multi-party chat dialogues](https://md.research.elamur.ai/free/en/papers/afantenos-2015-discourse-parsing-multi-party-chat/)
- [Learning Connective-based Word Representations for Implicit Discourse Relation Identification](https://md.research.elamur.ai/free/en/papers/braud-2016-learning-connective-based-word-representations/)
- [Cross-lingual RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/braud-2017-cross-lingual-rst-discourse-parsing/)
- [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/)
- [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/)
- [HILDA: A Discourse Parser Using Support Vector Machine Classification](https://md.research.elamur.ai/free/en/papers/hernault-2010-hilda-discourse-parser-support-vector/)
- [MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision](https://md.research.elamur.ai/free/en/papers/huber-2020-mega-rst-discourse-treebanks-structure/)
- [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/)
- [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/)
- [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://md.research.elamur.ai/free/en/papers/liu-2016-recognizing-implicit-discourse-relations-repeated/)
- [Learning Contextually Informed Representations for Linear-Time Discourse Parsing](https://md.research.elamur.ai/free/en/papers/liu-2017-learning-contextually-informed-representations-linear/)
- [Can we obtain significant success in RST discourse parsing by using Large Language Models?](https://md.research.elamur.ai/free/en/papers/maekawa-2024-can-we-obtain-significant-success/)
- [Constrained Decoding for Text-Level Discourse Parsing](https://md.research.elamur.ai/free/en/papers/muller-2012-constrained-decoding-text-level-discourse/)
- [Integer Linear Programming for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/perret-2016-integer-linear-programming-discourse-parsing/)
- [Lexicon-Based Methods for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/)
- [The CoNLL-2015 Shared Task on Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/xue-2015-conll-2015-shared-task-shallow/)

## Cite

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

```bibtex
@inproceedings{subba2009effective,
  title = {{An effective Discourse Parser that uses Rich Linguistic Information}},
  author = {Subba, Rajen and Eugenio, Barbara Di},
  booktitle = {Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  year = {2009},
  doi = {10.3115/1620754.1620837},
  url = {https://doi.org/10.3115/1620754.1620837}
}
```

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