--- title: "Shallow Discourse Parsing Using Constituent — Chen 2015" description: "This paper presents a shallow discourse parser built for the closed track of the CoNLL-2015 shared task." authors: ["Changge Chen", "Peilu Wang", "Hai Zhao"] year: 2015 venue: "Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task" source: "https://aclanthology.org/K15-2005/" doi: "10.18653/v1/k15-2005" 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/chen-2015-shallow-discourse-parsing-constituent-parsing/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/chen-2015-shallow-discourse-parsing-constituent-parsing/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2015](https://md.research.elamur.ai/free/papers/2015/page/2/) / Shallow Discourse Parsing Using Constituent Parsing Tree # Shallow Discourse Parsing Using Constituent Parsing Tree **Authors:** Changge Chen, Peilu Wang, [Hai Zhao](https://md.research.elamur.ai/free/authors/hai-zhao/) · **Year:** 2015 · **Venue:** Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task · **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/K15-2005/) · [PDF](https://aclanthology.org/K15-2005.pdf) · [DOI 10.18653/v1/k15-2005](https://doi.org/10.18653/v1/k15-2005) · [OpenAlex](https://openalex.org/W2250881903) **Citations:** 8 citations (openalex-snapshot · 2026-10-14) · [7](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This paper presents a shallow discourse parser built for the closed track of the CoNLL-2015 shared task. It identifies discourse relations in text, along with their arguments and senses, using only organizer-provided resources such as the Penn Discourse Treebank (PDTB). Following the annotation-mimicking design of Lin et al. (2014), the authors split the problem into a chain of classification steps, each handled by a maximum entropy model. The explicit branch finds connectives, labels their Arg1/Arg2 spans using a constituent-tree method adapted from Kong et al. (2014), and assigns each connective its most frequent sense. The non-explicit branch filters candidate pairs of adjacent sentences, ignores the rare 'AltLex' type, and treats 'EntRel' as one more implicit sense. Connective detection worked reasonably well, but argument extraction was weak, and these errors propagated through the pipeline, giving very low end-to-end scores. The paper is mainly useful as a baseline and as a case study of how errors compound in sequential discourse parsing. ## Key points - Connective candidates are found by a level-order traversal of the parse tree. This recovers 13,911 of 14,722 explicit connectives in the training set. The connective classifier then reaches 93.87% accuracy and 90.1% F1 on dev. - The authors re-implemented the argument pruning of Kong et al. (2014), but it gave only 37.5% (Arg1) and 51.3% (Arg2) recall. Adding the rest of the sentence after the connective as an extra Arg2 candidate raised Arg2 recall to 93.1%. - On gold-pruned candidates, the argument labeler reached F1 of 0.7645 for Arg1 and 0.8812 for Arg2. - In the training data, non-explicit relations are 73.85% 'Implicit', 23.2% 'EntRel' and 2.94% 'AltLex'. Filtering leaves 8,728 non-explicit relations. - Official results: - Explicit connective F1 was 0.8168 on blind test, 0.7867 on test and 0.8609 on dev. - Joint Arg1+Arg2 extraction F1 was only about 0.05. - Overall parser F1 was 0.0358 on blind test, 0.0443 on test and 0.0655 on dev. *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 7 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 1 not yet. - [Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/bai-2018-deep-enhanced-representation-implicit-discourse/) - [A Constituent Syntactic Parse Tree Based Discourse Parser](https://md.research.elamur.ai/free/papers/li-2016-constituent-syntactic-parse-tree-based/) - [Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings](https://md.research.elamur.ai/free/papers/qin-2016-implicit-discourse-relation-recognition-context/) - [Shallow Discourse Parsing Using Convolutional Neural Network](https://md.research.elamur.ai/free/papers/qin-2016-shallow-discourse-parsing-convolutional-neural/) - [A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/papers/qin-2016-stacking-gated-neural-architecture-implicit/) - [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/papers/qin-2017-adversarial-connective-exploiting-networks-implicit/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{chen2015shallow, title = {{Shallow Discourse Parsing Using Constituent Parsing Tree}}, author = {Chen, Changge and Wang, Peilu and Zhao, Hai}, booktitle = {Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task}, year = {2015}, doi = {10.18653/v1/k15-2005}, url = {https://doi.org/10.18653/v1/k15-2005} } ``` - [← previous Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhang-2015-shallow-convolutional-neural-network-implicit/) - [next → Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://md.research.elamur.ai/free/papers/xu-2015-show-attend-tell-neural-image/) - [↑ back to papers of 2015](https://md.research.elamur.ai/free/papers/2015/page/2/) ## References 10 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Zhongye Jia, Peilu Wang, and Hai Zhao. 2013. Grammatical error correction as multiclass classification with single model. pages 74–81, Sofia, Bulgaria, August. [find](https://scholar.google.com/scholar?q=Zhongye+Jia%2C+Peilu+Wang%2C+and+Hai+Zhao%2E+2013%2E+Grammatical+error+correction+as+multiclass+classification+with+single+model%2E+pages+74%E2%80%9381%2C+Sofia%2C+Bulgaria%2C+August%2E) 2. Fang Kong, Hwee Tou Ng, and Guodong Zhou. 2014. A constituent-based approach to argument labeling with joint inference in discourse parsing. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 68–77, Doha, Qatar, October. 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In 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, pages 13–16, Suntec, Singapore… [find](https://scholar.google.com/scholar?q=Emily+Pitler+and+Ani+Nenkova%2E+2009%2E+Using+syntax+to+disambiguate+explicit+discourse+connectives+in+text%2E+In+Proceedings+of+the+Joint+Conference+of+the+47th+Annual+Meeting+of+the+ACL+and+the+4th+Intern) [our card](https://md.research.elamur.ai/free/papers/pitler-2009-syntax-disambiguate-explicit-discourse-connectives/) 5. Emily Pitler, Annie Louis, and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. 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