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 36. Discourse relations and coherence, and 24. Coordination and 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

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/}
}

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.

1. E. Black, S. Abney, D. Flickinger, C. Gdaniec, R. Grishman, P. Harrison, D. Hindle, R. Ingria, F. Jelinek, J. Klavans, M. Liberman, M. Marcus, S. Roukos, B. Santorini, and T. Strzalkowski. 1991. A procedure for quantitatively comparing the syntactic coverage of English grammars. In Proceedings of Speech and Natural Language Workshop, pages 306–311, Pacific Groove, CA. DARPA. find
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. find our card
3. Eugene Charniak. 2000. A maximum-entropy-inspired parser. In Proceedings of the NAACL 2000, pages 132– 139, Seattle, Washington, April 29 – May 3. find
4. Michael Collins. 2000. Discriminative reranking for natural language parsing. In Proceedings of ICML 2000, Stanford University, Palo Alto, CA, June 29–July 2. find our card
5. C. J. Fillmore, C. F. Baker, and S. Hiroaki. 2002. The framenet database and software tools. In Proceedings of the LREC 2002, pages 1157–1160, LREC. find
6. K. Forbes, E. Miltsakaki, R. Prasad, A. Sarkar, A. Joshi, and B. Webber. 2001. D-LTAG System: Discourse parsing with a lexicalized tree-adjoining grammar. In ESSLLI’2001 Workshop on Information Structure, Discourse Structure and Discourse Semantics. find
7. Daniel Gildea and Daniel Jurafsky. 2002. Automatic labeling of semantic role. Computational Linguistics, 28(3):245–288. find
8. Paul Kingsbury and Martha Palmer. 2002. From Treebank to Propbank. In Proceedings of the LREC 2002, Las Palmas, Canary Islands, Spain, May 28-June 3. find our card
9. David M. Magerman. 1995. Statistical decision-tree models for parsing. In Proceedings of the ACL 1995, pages 276–283, Cambridge, Massachusetts, June 26-30. find
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. find
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. find our card
13. Senko K. Maynard. 1998. Principles of Japanese Discourse: A Handbook. Cambridge University Press. find
14. David D. Palmer and Marti A. Hearst. 1997. Adaptive multilingual sentence boundary disambiguation. Computational Linguistics, 23(2):241–269, June. find
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. ↗

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/soricut-2003-sentence-level-discourse-parsing-syntactic/