--- title: "The Effects of Discourse Connectives Prediction — Zhou 2010" description: "Without an overt connective such as \"but\" or \"because\", recognizing the discourse relation between two text spans is hard." authors: ["Zhi Min Zhou", "Man Lan", "Zheng Yu Niu", "Yu Xu", "Jian Su"] year: 2010 venue: "Proceedings of the SIGDIAL 2010 Conference" source: "https://aclanthology.org/W10-4326/" 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/zhou-2010-effects-discourse-connectives-prediction-implicit/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/zhou-2010-effects-discourse-connectives-prediction-implicit/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2010](https://md.research.elamur.ai/free/papers/2010/) / The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition # The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition **Authors:** [Zhi Min Zhou](https://md.research.elamur.ai/free/authors/zhi-min-zhou/), [Man Lan](https://md.research.elamur.ai/free/authors/man-lan/), [Zheng Yu Niu](https://md.research.elamur.ai/free/authors/zheng-yu-niu/), [Yu Xu](https://md.research.elamur.ai/free/authors/yu-xu/), [Jian Su](https://md.research.elamur.ai/free/authors/jian-su/) · **Year:** 2010 · **Venue:** Proceedings of the SIGDIAL 2010 Conference · **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/W10-4326/) · [PDF](https://aclanthology.org/W10-4326.pdf) · [OpenAlex](https://openalex.org/W21113628) **Citations:** 13 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Without an overt connective such as "but" or "because", recognizing the discourse relation between two text spans is hard. Connectives carry most of the signal: the authors show that mapping gold implicit connectives in PDTB to their most frequent sense yields about 91.8% averaged F-score. They therefore try to recover the missing connective automatically. For each candidate connective, they build two synthetic sentences, one with the connective placed before Arg1 and one with it between Arg1 and Arg2. An n-gram language model trained on unannotated news text scores each sentence, and the candidates with the lowest perplexity are taken as predictions. These predictions feed linear SVMs in two ways: - **Sense frequency:** a 4-dimensional vector holding the training-set sense counts of the top-ranked connective. - **Presence:** binary indicators for the top N predicted connectives, which the authors describe as the unsupervised variant. On PDTB 2.0, the approach comes close to the feature-rich state of the art and beats it on Comparison. It uses only a handful of features, and because it depends mainly on raw text, it should transfer more easily to other languages and domains. ## Key points - The evaluation uses four one-vs-rest binary classifiers (Comparison, Contingency, Expansion, Temporal), built as linear SVMs with LIBSVM. Training sets are balanced, and PDTB sections 21–22 serve as the test set. - On implicit relations, the best averaged F1 is 35.55, from the presence-of-top-N features, against 40.57 for Pitler et al. (2009a). For Comparison, the sense-frequency approach reaches 26.02 F1 versus 21.96, a gain of more than 4 points. - The language models were trained on news corpora (New York, Xin, Ltw) from the North American News collection. 4-gram models gave roughly 0.5% better averaged F-score than 3-gram models. The best values of N mostly fell between 100 and 160. - The authors also converted explicit relations into implicit-like examples by deleting the connective. On this data, averaged F1 reached 39.98, compared with about 33.69 that Sporleder and Lascarides (2008) reported on different data. - The method needs at most about 200 features, whereas linguistically informed systems use thousands. It also requires no parsers or other heavy NLP tools. *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 13 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 6 not yet. - [Implicitness of Discourse Relations](https://md.research.elamur.ai/free/papers/asr-2012-implicitness-discourse-relations/) - [Disambiguating Explicit Discourse Connectives without Oracles](https://md.research.elamur.ai/free/papers/johannsen-2013-disambiguating-explicit-discourse-connectives-without/) - [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://md.research.elamur.ai/free/papers/liu-2023-annotation-inspired-implicit-discourse-relation/) - [Automatic Prediction of Discourse Connectives](https://md.research.elamur.ai/free/papers/malmi-2018-automatic-prediction-discourse-connectives/) - [Implicit Discourse Relation Recognition by Selecting Typical Training Examples](https://md.research.elamur.ai/free/papers/wang-2012-implicit-discourse-relation-recognition-selecting/) - [Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation](https://md.research.elamur.ai/free/papers/wu-2023-connective-prediction-implicit-discourse-relation/) - [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhou-2022-prompt-based-connective-prediction-method/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zhou2010effects, title = {{The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition}}, author = {Zhou, Zhi Min and Lan, Man and Niu, Zheng Yu and Xu, Yu and Su, Jian}, booktitle = {Proceedings of the SIGDIAL 2010 Conference}, year = {2010}, url = {https://aclanthology.org/W10-4326/} } ``` - [← previous The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text](https://md.research.elamur.ai/free/papers/farkas-2010-conll-2010-shared-task-learning/) - [next → The Leeds Arabic Discourse Treebank: Annotating Discourse Connectives for Arabic](https://md.research.elamur.ai/free/papers/saif-2010-leeds-arabic-discourse-treebank-annotating/) - [↑ back to papers of 2010](https://md.research.elamur.ai/free/papers/2010/) ## References 20 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. J. 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