Learning Accurate, Compact, and Interpretable Tree Annotation
Open accessAbstract
We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple X-bar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals. In contrast with previous work, we are able to split various terminals to different degrees, as appropriate to the actual complexity in the data. Our grammars automatically learn the kinds of linguistic distinctions exhibited in previous work on manual tree annotation. On the other hand, our grammars are much more compact and substantially more accurate than previous work on automatic annotation. Despite its simplicity, our best grammar achieves an F1 of 90.2% on the Penn Treebank, higher than fully lexicalized systems.
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The Transformer outperforms the BerkeleyParser [29] even when training only on the WSJ training set of 40K sentences.
“In contrast to RNN sequence-to-sequence models [37], the Transformer outperforms the BerkeleyParser [29] even when training only on the WSJ training set of 40K sentences.”
From Attention Is All You Need (2017) · cites this paper as [29]
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- Authors
- Petrov, S., Barrett, L., Thibaux, R., & Klein, D.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
- Published
- 2006
- Pages
- 433-440
- ISSN
- 1525-2477 (ISSN-L)
- OpenAlex
- W2139621418
- Cited by
- 793
- References
- 18
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 9 · 2022: 7 · 2021: 16 · 2020: 22 · 2019: 24 · 2018: 33 · 2017: 36 · 2016: 72 · 2015: 80 · 2014: 87 · 2013: 91 · 2012: 103
- FWCI
- 18.21
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Computational Linguistics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- tree annotation, grammar learning, Penn Treebank, lexicalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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