---
title: "Unsupervised Discovery of Discourse Relations — Zhou 2011"
description: "Sentences that mix positive and negative opinions are hard for sentence-level polarity classifiers."
authors: ["Lanjun Zhou", "Binyang Li", "Wei Gao", "Zhongyu Wei", "Kam-Fai Wong"]
year: 2011
venue: "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D11-1015/"
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-2011-unsupervised-discovery-discourse-relations-eliminating/"
---
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# Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities

**Authors:** [Lanjun Zhou](https://md.research.elamur.ai/free/authors/lanjun-zhou/), [Binyang Li](https://md.research.elamur.ai/free/authors/binyang-li/), [Wei Gao](https://md.research.elamur.ai/free/authors/wei-gao/), [Zhongyu Wei](https://md.research.elamur.ai/free/authors/zhongyu-wei/), [Kam-Fai Wong](https://md.research.elamur.ai/free/authors/kam-fai-wong/) · **Year:** 2011 · **Venue:** Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing · **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/D11-1015/) · [PDF](https://aclanthology.org/D11-1015.pdf) · [OpenAlex](https://openalex.org/W1865572304)

**Citations:** 92 citations (openalex-snapshot · 2026-10-14) · [18](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Sentences that mix positive and negative opinions are hard for sentence-level polarity classifiers. A sentence can carry more negative words than positive ones and still be positive overall. This paper uses the discourse structure inside a sentence to decide which clause controls the overall sentiment. It targets Chinese news text from the NTCIR MOAT corpora.

The authors derive a coarse scheme from Rhetorical Structure Theory with five relations: Contrast, Condition, Continuation, Cause and Purpose. Each relation comes with rules about how the polarities of its segments interact. For example, the nucleus decides the polarity for most relations.

Starting from a handful of cue-phrase patterns, they harvest many clause pairs from raw text. These pairs are encoded as semantic sequential representations (SSRs) built from part-of-speech, semantic and sentiment tags. An unsupervised learner then generates, weights and filters common SSRs, so that relations can be recognized even when no cue phrase is present. The SSRs also serve as SVM features.

Adding the recovered discourse information gives a statistically significant gain over a strong baseline. Because no annotated discourse treebank is required, the approach should transfer to other languages.

## Key points
- About 43% of opinionated sentences in the NTCIR-6/7 MOAT Chinese corpora contain mixed polarity. Ambiguous sentences account for 49% of the errors made by the baseline polarity classifier (BPC), which is the Xu and Kit (2010) system from NTCIR-8 MOAT.
- The scheme was built by annotating 500 ambiguous sentences. Thirteen RST relations covered roughly 70% of the useful discourse links, and these were merged into 5 coarse relations.
- Clauses are split at commas and semicolons. Each harvested segment pair must contain at least one sentiment word per segment.
- Weighting and filtering of mined SSRs (cSSR) raise precision from 0.6182 to 0.8864 and F-score from 0.5537 to 0.6293. They also shrink the SSR set from over 1 million to about 0.12 million, at a small cost in recall.
- SVM+SSRs significantly outperform BPC in polarity classification (p\<0.01). Most of the gains come from the Contrast, Continuation and Cause relations.

*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

18 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 15 not yet.

- [Better Document-level Sentiment Analysis from RST Discourse Parsing](https://md.research.elamur.ai/free/papers/bhatia-2015-better-document-level-sentiment-analysis/)
- [Exploiting Discourse Relations for Sentiment Analysis](https://md.research.elamur.ai/free/papers/wang-2012-exploiting-discourse-relations-sentiment-analysis/)
- [Cross-Lingual Identification of Ambiguous Discourse Connectives for Resource-Poor Language](https://md.research.elamur.ai/free/papers/zhou-2012-cross-lingual-identification-ambiguous-discourse/)

## Cite

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

```bibtex
@inproceedings{zhou2011unsupervised,
  title = {{Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities}},
  author = {Zhou, Lanjun and Li, Binyang and Gao, Wei and Wei, Zhongyu and Wong, Kam-Fai},
  booktitle = {Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing},
  year = {2011},
  url = {https://aclanthology.org/D11-1015/}
}
```

- [← previous Unsupervised Decomposition of a Document into Authorial Components](https://md.research.elamur.ai/free/papers/aclbulk-2011-koppel-unsupervised-decomposition-document-into-authorial/)
- [↑ back to papers of 2011](https://md.research.elamur.ai/free/papers/2011/)

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