--- title: "Classification of Discourse Coherence — Wellner 2006" description: "This study tackles two tasks on the Discourse GraphBank (Wolf and Gibson, 2005), a corpus of 135 news articles." authors: ["Ben Wellner", "James Pustejovsky", "Catherine Havasi", "Anna Rumshisky", "Roser Saurí"] year: 2006 venue: "Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue" source: "https://aclanthology.org/W06-1317/" 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/papers/wellner-2006-classification-discourse-coherence-relations-exploratory/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/wellner-2006-classification-discourse-coherence-relations-exploratory/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2006](https://md.research.elamur.ai/free/papers/2006/) / Classification of Discourse Coherence Relations: An Exploratory Study using Multiple Knowledge Sources # Classification of Discourse Coherence Relations: An Exploratory Study using Multiple Knowledge Sources **Authors:** Ben Wellner, [James Pustejovsky](https://md.research.elamur.ai/free/authors/james-pustejovsky/), Catherine Havasi, [Anna Rumshisky](https://md.research.elamur.ai/free/authors/anna-rumshisky/), Roser Saurí · **Year:** 2006 · **Venue:** Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue · **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/W06-1317/) · [PDF](https://aclanthology.org/W06-1317.pdf) · [OpenAlex](https://openalex.org/W7100313604) **Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This study tackles two tasks on the Discourse GraphBank (Wolf and Gibson, 2005), a corpus of 135 news articles. Its discourse relations form graphs rather than trees, because segments can have several parents and links can cross. The tasks are (1) labelling the type of coherence relation between two segments and (2) deciding whether two segments are related at all. Gold discourse segments are assumed. Instead of a compositional discourse parser, the authors use a flat Maximum Entropy classifier. It combines many knowledge sources: * RASP grammatical relations * Carafe CRF tagging and chunking * EvITA event features, SlinkET modal subordination and temporal links (from the TARSQI framework) * Word Sketch Engine similarity computed over the British National Corpus * the Brandeis Semantic Ontology (BSO) The classifier reaches about 81% accuracy on relation typing. On identifying relations within a single sentence it reaches about 70%. The authors also argue that GraphBank's cause-effect label wrongly merges true causation with purpose and rationale clauses. The work shows that a simple feature-based model can absorb diverse linguistic resources, and its feature analysis can guide more structured approaches such as SDRT or D-LTAG. ## Key points - **Data:** Discourse GraphBank, 135 documents, with 11 relation labels. The most frequent are elab, attr, par, same and ce. - **Relation typing:** 81.06% fine-grained and 87.51% coarse-grained accuracy using all feature classes. - **Per-relation results:** attr and elab score F above 91. Rare relations perform poorly; for example, temp scores about 29 F. - **Ablation:** Removing single feature classes shows that some contribute far more than others. Dropping the "P" class lowers accuracy to 71.52%. - **Relation identification (same-sentence pairs):** 70.04% accuracy, and 64.53% when identification and typing are done jointly. The baseline is 58%. - **Dependency features:** Adding features from neighbouring gold relations raises typing accuracy to 82.3%. *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/shelves/linguistics/) Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/) ## Cited by Cited by 25 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 12 not yet. - [Automatic sense prediction for implicit discourse relations in text](https://md.research.elamur.ai/free/papers/disc-2009-pitler-automatic-sense-prediction-implicit-discourse/) - [Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/lan-2013-leveraging-synthetic-discourse-data-multi/) - [Recognizing Implicit Discourse Relations in the Penn Discourse Treebank](https://md.research.elamur.ai/free/papers/lin-2009-recognizing-implicit-discourse-relations-penn/) - [Implicit Discourse Relation Identification for Open-domain Dialogues](https://md.research.elamur.ai/free/papers/ma-2019-implicit-discourse-relation-identification-open/) - [Multilingual Annotation and Disambiguation of Discourse Connectives for Machine Translation](https://md.research.elamur.ai/free/papers/meyer-2011-multilingual-annotation-disambiguation-discourse-connectives/) - [Using Sense-labeled Discourse Connectives for Statistical Machine Translation](https://md.research.elamur.ai/free/papers/meyer-2012-sense-labeled-discourse-connectives-statistical/) - [Constrained Decoding for Text-Level Discourse Parsing](https://md.research.elamur.ai/free/papers/muller-2012-constrained-decoding-text-level-discourse/) - [Easily Identifiable Discourse Relations](https://md.research.elamur.ai/free/papers/pitler-2008-easily-identifiable-discourse-relations/) - [Using Syntax to Disambiguate Explicit Discourse Connectives in Text](https://md.research.elamur.ai/free/papers/pitler-2009-syntax-disambiguate-explicit-discourse-connectives/) - [Modelling Discourse Relations for Arabic](https://md.research.elamur.ai/free/papers/saif-2011-modelling-discourse-relations-arabic/) - [An effective Discourse Parser that uses Rich Linguistic Information](https://md.research.elamur.ai/free/papers/subba-2009-effective-discourse-parser-that-uses/) - [The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhou-2010-effects-discourse-connectives-prediction-implicit/) - [Predicting Discourse Connectives for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhou-2010-predicting-discourse-connectives-implicit-discourse/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{wellner2006classification, title = {{Classification of Discourse Coherence Relations: An Exploratory Study using Multiple Knowledge Sources}}, author = {Wellner, Ben and Pustejovsky, James and Havasi, Catherine and Rumshisky, Anna and Saurí, Roser}, booktitle = {Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue}, year = {2006}, url = {https://aclanthology.org/W06-1317/} } ``` - [← previous CDER: Efficient MT Evaluation Using Block Movements](https://md.research.elamur.ai/free/papers/leusch-2006-cder-efficient-mt-evaluation-block/) - [next → Computing relative polarity for textual inference](https://md.research.elamur.ai/free/papers/lingbulk-2006-nairn-computing-relative-polarity-textual-inference/) - [↑ back to papers of 2006](https://md.research.elamur.ai/free/papers/2006/) ## References 19 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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