---
title: "Automatic sense prediction for implicit discourse relations in text — Pitler 2009"
description: "It tests whether word-pair features from earlier work help. 23 references, cited by 142 papers here."
authors: ["Emily Pitler", "Annie Louis", "Ani Nenkova"]
year: 2009
venue: "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"
source: "https://aclanthology.org/P09-1077/"
doi: "10.3115/1690219.1690241"
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/en/papers/pitler-2009-automatic-sense-prediction-implicit-discourse/"
---
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# Automatic sense prediction for implicit discourse relations in text

**Authors:** [Emily Pitler](https://md.research.elamur.ai/free/en/authors/emily-pitler/), [Annie Louis](https://md.research.elamur.ai/free/en/authors/annie-louis/) and [Ani Nenkova](https://md.research.elamur.ai/free/en/authors/ani-nenkova/) · **Year:** 2009 · **Venue:** Proceedings of ACL-IJCNLP 2009 **Link:** [aclanthology.org/P09-1077](https://aclanthology.org/P09-1077/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**276** citations](https://openalex.org/works?filter=cites:W2109462987) OpenAlex, 2026-10-14 · [**142** citations in our library](https://md.research.elamur.ai/free/en/papers/pitler-2009-automatic-sense-prediction-implicit-discourse/cited-by/) of the 140,884 papers whose bibliographies we read · [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](https://aclanthology.org/volumes/P09-1/) · [licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P09-1077/)
- [PDF](https://aclanthology.org/P09-1077.pdf)
- [DOI 10.3115/1690219.1690241](https://doi.org/10.3115/1690219.1690241)
- [OpenAlex](https://openalex.org/W2109462987)

**Authors (3)**

1. [Emily Pitler](https://openalex.org/A5089519482) [Semantic Scholar](https://www.semanticscholar.org/author/2585932) · h 24 · 3,809 citations · 55 works
2. [Annie Louis](https://openalex.org/A5112136402) [ORCID](https://orcid.org/0000-0003-4226-252X) · [Semantic Scholar](https://www.semanticscholar.org/author/1767336) · h 22 · 2,996 citations · 87 works
3. [Ani Nenkova](https://openalex.org/A5032571629) [ORCID](https://orcid.org/0000-0002-5825-7875) · [Semantic Scholar](https://www.semanticscholar.org/author/3115414) · h 47 · 10,177 citations · 199 works

Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart.

## What it is about

The paper predicts the sense (Comparison, Contingency, Expansion, Temporal) of implicit discourse relations, meaning adjacent sentences with no connective, in PDTB newswire. It tests whether word-pair features from earlier work help. Word pairs from artificial implicit data (explicit connectives removed) learn the connectives that were removed, and their top pairs are function-word pairs such as "but-but", so they do not capture semantic opposition. The authors add polarity tags, Levin verb classes, first and last words, modality, number features and context (a neighbouring explicit relation or a paragraph boundary). The best model reaches F about 47 for Contingency (16 points over the word-pair baseline) and about 76 for Expansion. Evaluation uses the natural class distribution, with sections 2-20 for training and 21-22 for testing.

## Ideas

- I4. Report per-sense F and a majority baseline for every relation set, not accuracy alone. Expansion dominates the distribution, so accuracy by itself hides a weak classifier.

## For Mova

- I1. Leakage audit for Mova's own training data. When the explicit connective is removed from a tale or school sentence to make a synthetic relation, check whether a pair model still sees the connective. Run the pair check first: if the top features are connective pairs, the synthetic data is leaking. Measure with two-fold `cv` and two identical runs.
- I2. Polarity pairs from the `en` lexicon as a Contingency signal. The paper finds opposite-polarity pairs help Contingency, not Comparison. Test this on our tale text, one relation at a time, and report per-relation F rather than total accuracy.
- I3. Neighbour context for implicit links in `prag`. If the previous link is explicit, use its connective and sense as a prior for the next implicit one. Measure on world paragraphs and report the gain only after two identical runs.

The chapter is [languages/en/36-discourse-relations.md](https://mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/), section 7 (implicit relations). The code is the link labels in `prag` and the word-pair and polarity features would live in a new module next to them. The paper's datasets (PDTB, TextRels, the Wilson polarity lexicon and the General Inquirer) need a licence check before use; the paper's own experiments are research-only, so use them as a reference, not as training data in a release.

## Institutions

[🎓**University of Pennsylvania** Philadelphia · United States 143 works · 56,645 citations](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/)

Topics[Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cite

Cite as Emily Pitler, Annie Louis and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. 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.

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1690219.1690241](https://doi.org/10.3115/1690219.1690241)

```bibtex
@inproceedings{pitler2009automatic,
  title = {{Automatic sense prediction for implicit discourse relations in text}},
  author = {Emily Pitler, Annie Louis and Ani Nenkova},
  booktitle = {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},
  year = {2009},
  doi = {10.3115/1690219.1690241},
  url = {https://doi.org/10.3115/1690219.1690241}
}
```

- [← previous Phrase Dependency Parsing for Opinion Mining](https://md.research.elamur.ai/free/en/papers/wu-2009-phrase-dependency-parsing-opinion-mining/)
- [next → Recognizing Implicit Discourse Relations in the Penn Discourse Treebank](https://md.research.elamur.ai/free/en/papers/lin-2009-recognizing-implicit-discourse-relations-penn/)
- [↑ back to papers of 2009](https://md.research.elamur.ai/free/en/papers/2009/)

## References

23 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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