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# The Penn Discourse TreeBank 2.0.

**Authors:** [Rashmi Prasad](https://md.mova.elamur.ai/free/en/docs/papers/authors/rashmi-prasad/), Nikhil Dinesh, [Alan Lee](https://md.mova.elamur.ai/free/en/docs/papers/authors/alan-lee/), [Eleni Miltsakaki](https://md.mova.elamur.ai/free/en/docs/papers/authors/eleni-miltsakaki/), Livio Robaldo, [Aravind Joshi](https://md.mova.elamur.ai/free/en/docs/papers/authors/aravind-joshi/), [Bonnie Webber](https://md.mova.elamur.ai/free/en/docs/papers/authors/bonnie-webber/) · **Year:** 2008 · **Venue:** Proceedings of LREC'08 **Link:** [aclanthology.org/L08-1093](https://aclanthology.org/L08-1093/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016); ELRA: all rights reserved — summary only. The PDTB data itself is LDC-licensed: tests only.

[**687** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/disc-2008-prasad-penn-discourse-treebank-2-0/) of the 140673 papers whose bibliographies we read[Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)](https://aclanthology.org/volumes/L08-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/L08-1093/)
- [PDF](https://aclanthology.org/L08-1093.pdf)

**Authors (7)**

1. Rashmi Prasad
2. Nikhil Dinesh
3. Alan Lee
4. Eleni Miltsakaki
5. Livio Robaldo
6. Aravind Joshi
7. Bonnie Webber

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

PDTB-2.0 is a lexically grounded annotation of discourse relations over the 1-million-word Wall Street Journal corpus. Each relation has two abstract-object arguments (Arg1, Arg2), and an explicit connective (18,459 tokens) or an inserted implicit connective (16,224) marks it; the rest are AltLex, EntRel and NoRel. Each relation has a three-level sense (class, type, subtype), an attribution record (source, type of attribution, scopal polarity, determinacy), and its arguments can lie far from the connective. Agreement is 90.2% exact-match for explicit arguments and 85.1% for implicit ones, and 94%, 84% and 80% at the class, type and subtype levels. The paper is a resource description with statistics, not a method.

## Ideas

- I1. A sense table for Mova's explicit connectives, with the set of senses each one can carry (since, while, although, if). Let the frame reader pick a sense from the frames of the two arguments and report the sense set when it cannot choose. Measure per-connective sense accuracy on PDTB sections as a test only (LDC data).
- I2. Arg1 search window for explicit connectives. Subordinating connectives take their own clause as Arg2 and the other clause as Arg1, which is often in an earlier sentence. Let the link search for Arg1 in the previous sentence first, then in earlier sentences, and log how far it reached. Measure the distance distribution on our own text; do not assume the WSJ numbers hold for tales.
- I3. Attribution as a frame feature: each relation records whether its source is the writer, another named agent, or an unnamed one. The verb classes (say, believe, know, want) can come from the speech-act code in `prag`. A relation attributed to someone else must not be counted as the writer's commitment in the output.
- I4. Report at the class level when unsure. The hierarchy lets the reader answer "TEMPORAL, order unknown" instead of guessing a subtype. Score class-level and subtype-level answers separately so the trade-off is visible.

## For Mova

The chapter is [36. Discourse relations and coherence](https://md.mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/) (sections 4, 6, 7 and 11). The code is the frame and link code in `prag` and the connective handling in `en` (grep for the connective list before adding anything). The PDTB text and its data are LDC and NC: tests only, never in a release, and no PDTB examples in the repo.

Topics: [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/) · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

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

```
@inproceedings{prasad2008penn,
  title = {{The Penn Discourse TreeBank 2.0.}},
  author = {Prasad, Rashmi and Dinesh, Nikhil and Lee, Alan and Miltsakaki, Eleni and Robaldo, Livio and Joshi, Aravind and Webber, Bonnie},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  url = {https://aclanthology.org/L08-1093/}
}
```

- [← previous The Use of Parallel and Comparable Data for Analysis of Abstract Anaphora in German and English](https://md.mova.elamur.ai/free/en/docs/papers/dipper-2012-use-parallel-comparable-data-analysis/)
- [next → Automatic sense prediction for implicit discourse relations in text](https://md.mova.elamur.ai/free/en/docs/papers/disc-2009-pitler-automatic-sense-prediction-implicit-discourse/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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