--- title: "Learning Recursive Segments for Discourse — Afantenos 2010" description: "Most earlier work on discourse segmentation was developed on the RST corpus, where elementary discourse units (EDUs) never nest." authors: ["Stergos Afantenos", "Pascal Denis", "Philippe Muller", "Laurence Danlos"] year: 2010 venue: "Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)" source: "https://aclanthology.org/L10-1401/" arxiv: "1003.5372" 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/afantenos-2010-learning-recursive-segments-discourse-parsing/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/afantenos-2010-learning-recursive-segments-discourse-parsing/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2010](https://md.research.elamur.ai/free/papers/2010/) / Learning Recursive Segments for Discourse Parsing # Learning Recursive Segments for Discourse Parsing **Authors:** [Stergos Afantenos](https://md.research.elamur.ai/free/authors/stergos-afantenos/), [Pascal Denis](https://md.research.elamur.ai/free/authors/pascal-denis/), [Philippe Muller](https://md.research.elamur.ai/free/authors/philippe-muller/), [Laurence Danlos](https://md.research.elamur.ai/free/authors/laurence-danlos/) · **Year:** 2010 · **Venue:** Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10) · **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/L10-1401/) · [PDF](https://aclanthology.org/L10-1401.pdf) · [arXiv 1003.5372](https://arxiv.org/abs/1003.5372) · [OpenAlex](https://openalex.org/W1517148511) **Citations:** 27 citations (openalex-snapshot · 2026-10-14) · [15](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Most earlier work on discourse segmentation was developed on the RST corpus, where elementary discourse units (EDUs) never nest. That allowed segmentation to be treated as binary boundary detection. SDRT-style annotation, however, permits embedded EDUs, for example for appositions and non-restrictive relatives. The authors address this setting using the first validated portion of the French Annodis corpus: 47 documents of Wikipedia and *L'est républicain* text, containing 1,445 EDUs, about 10% of them nested. Following the clause boundary identification approach from CoNLL-2001, each token is labelled as opening a segment, closing one, forming a one-token segment, or none of these. A regularized MaxEnt model trained with MegaM makes these predictions using lexical markers and morpho-syntactic features from the Macaon chunker and the Syntex dependency parser. Chunk boundaries guide a resampling step. A left-to-right and right-to-left repair heuristic then adds or removes brackets so the output is well formed. With repair, the system reaches roughly 73% F-score on complete EDUs, close to CBI results. This makes it a usable base for SDRT parsing and for pre-annotation. ## Key points - Data: 47 validated Annodis documents (14,384 tokens, about 33 EDUs per text). The full corpus is planned at 100–150 texts. - Features include lemma, POS, chunk tags, dependency paths (Syntex), outward chunk sequences, context n-grams (1 \< n ≤ 6), discourse connectives and reporting verbs. - Resampling relies on Macaon output: tokens inside chunks and sentence-boundary tokens are dropped from training. This cuts *nothing* instances from about 12,000 to about 9,200. - Without repair, EDU F-score is 0.575 (recall 0.427). With repair it rises to 0.733 (P 0.748, R 0.719), and 98% of segmentations are well formed. Left and right boundary F-scores are about 0.88 and 0.89. - Adding previous labels as features with Viterbi decoding reduced performance. The learning curve suggests more data would bring only modest gains. *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 15 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 11 not yet. - [EusDisParser: improving an under-resourced discourse parser with cross-lingual data](https://md.research.elamur.ai/free/papers/iruskieta-2019-eusdisparser-improving-under-resourced-discourse/) - [Annotation upon Annotation: Adding Signalling Information to a Corpus of Discourse Relations](https://md.research.elamur.ai/free/papers/taboada-2013-annotation-upon-annotation-adding-signalling/) - [The DISRPT 2019 Shared Task on Elementary Discourse Unit Segmentation and Connective Detection](https://md.research.elamur.ai/free/papers/zeldes-2019-disrpt-2019-shared-task-elementary/) - [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://md.research.elamur.ai/free/papers/zeldes-2025-erst-signaled-graph-theory-discourse/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{afantenos2010learning, title = {{Learning Recursive Segments for Discourse Parsing}}, author = {Afantenos, Stergos and Denis, Pascal and Muller, Philippe and Danlos, Laurence}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, eprint = {1003.5372}, archivePrefix = {arXiv}, url = {https://arxiv.org/abs/1003.5372} } ``` - [← previous Kernel Based Discourse Relation Recognition with Temporal Ordering Information](https://md.research.elamur.ai/free/papers/wang-2010-kernel-based-discourse-relation-recognition/) - [next → Learning to Translate with Source and Target Syntax](https://md.research.elamur.ai/free/papers/chiang-2010-learning-translate-source-target-syntax/) - [↑ back to papers of 2010](https://md.research.elamur.ai/free/papers/2010/) ## 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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