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# Sentence Level Discourse Parsing using Syntactic and Lexical Information

**Authors:** Radu Soricut, [Daniel Marcu](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-marcu/) · **Year:** 2003 · **Venue:** Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2003) **Link:** [aclanthology.org/N03-1030](https://aclanthology.org/N03-1030/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**158** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/soricut-2003-sentence-level-discourse-parsing-syntactic/) of the 140673 papers whose bibliographies we read[Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics](https://aclanthology.org/volumes/N03-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/N03-1030/)
- [PDF](https://aclanthology.org/N03-1030.pdf)

**Authors (2)**

1. Radu Soricut
2. Daniel Marcu

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

## What it is about

The paper builds sentence-level RST discourse trees in two steps. First a probabilistic segmenter finds elementary discourse units inside a sentence, using syntactic and lexical features. Then a probabilistic parser builds the tree, using "dominance sets" taken from the Penn Treebank parse. The data are 385 WSJ articles with RST annotation, used with a training part of 5,809 sentence triples and a test part of 946. On the test set, boundary recall rises from 77.1% (the earlier decision-based segmenter) to 82.7%, and the discourse parser's F-score rises from 37.2% to 49.0% with 18 labels (an error reduction of 18.8%). Human agreement is 77.0%. With perfect syntax and perfect segmentation together, the F-score reaches 75.5%. The authors conclude that segmentation, not the parser, is the bottleneck.

## Ideas

- I1. Start with a boundary baseline built on UD trees in `en/src/parse.rs` output: put a clause boundary before `advcl`, `ccomp`, `csubj` and `conj` subtrees that carry a finite verb, and compare it with a comma-only baseline. Report precision and recall, recall first, as the paper does.
- I2. Keep a deterministic dominance relation from the UD head–dependent structure (which span is the nucleus-like head, which is the satellite-like dependent) and count how often a rule agrees with the gold tree. Log each disagreement with its rule.
- I3. Report the segmenter's error by boundary type. The paper shows that recall is the weak point, so any Mova segmenter should show which boundary types it misses.
- I4. Measure the gap to the human ceiling on a small annotated set, not only the average, to see whether a rule set is "near human" on some sentence types.

## For Mova

The chapter is [36. Discourse relations and coherence](https://md.mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/), and [24. Coordination and subordination](https://md.mova.elamur.ai/free/en/docs/languages/en/24-coordination-subordination/) for the clause boundaries. No discourse module exists in the code yet, so check with grep first. The natural place is a new module in `prag` that reads the UD tree from `en/src/parse.rs`. The measure is boundary P/R/F1 on a held-out set. The RST corpus is licensed through its own distributor, so use it for evaluation only.

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

## Cite

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

```
@inproceedings{soricut2003sentence,
  title = {{Sentence Level Discourse Parsing using Syntactic and Lexical Information}},
  author = {Soricut, Radu and Marcu, Daniel},
  booktitle = {Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics},
  year = {2003},
  url = {https://aclanthology.org/N03-1030/}
}
```

- [← previous A Machine Learning Approach to Coreference Resolution of Noun Phrases](https://md.mova.elamur.ai/free/en/docs/papers/soon-2001-machine-learning-approach-coreference-resolution/)
- [next → PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities](https://md.mova.elamur.ai/free/en/docs/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

16 references, 5 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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