📄 Sentence Level Discourse Parsing using Syntactic and Lexical Information

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 languages/en/36-discourse-relations.md, and languages/en/24-coordination-subordination.md 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

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/1073445.1073475

@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},
  doi = {10.3115/1073445.1073475},
  url = {https://doi.org/10.3115/1073445.1073475}
}

References

16 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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2. L. Carlson, D. Marcu, and M. E. Okurowski. 2003. Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In Jan van Kuppevelt and Ronnie Smith, editors, Current Directions in Discourse and Dialogue. Kluwer Academic Publishers. To appear. ↗ our card
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10. William C. Mann and Sandra A. Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3):243–281. ↗ our card
11. Daniel Marcu. 2000. The Theory and Practice of Discourse Parsing and Summarization. The MIT Press, Cambridge, Massachusetts. ↗
12. M. Marcus, B. Santorini, and M. Marcinkiewicz. 1993. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313–330. ↗ our card
13. Senko K. Maynard. 1998. Principles of Japanese Discourse: A Handbook. Cambridge University Press. ↗
14. David D. Palmer and Marti A. Hearst. 1997. Adaptive multilingual sentence boundary disambiguation. Computational Linguistics, 23(2):241–269, June. ↗
15. Adwait Ratnaparkhi. 1998. Maximum Entropy Models for Natural Language Ambiguity Resolution. Ph.D. thesis, University of Pennsylvania. find
16. RST-DT. 2002. RST Discourse Treebank. Linguistic Data Consortium. http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2002T07. ↗

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