The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587
Accurate unlexicalized parsing
Klein, D., & Manning, C. D. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075096.1075150 · Cited by 3,055
Self-citationHead-Driven Statistical Models for Natural Language Parsing
Collins, M. (2003) · Computational Linguistics · doi:10.1162/089120103322753356 · Cited by 1,822
(1999) · Cited by 1,521
Automatic word sense discrimination
Schütze, H. (1998) · Cited by 1,269
A clustering technique for summarizing multivariate data
Ball, G. H., & Hall, D. J. (1967) · Systems Research and Behavioral Science · doi:10.1002/bs.3830120210 · Cited by 926
Coarse-to-fine n -best parsing and MaxEnt discriminative reranking
Eugene Charniak, D., & Johnson, M. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219862 · Cited by 878
PCFG models of linguistic tree representations
Johnson, M. (1998) · doi:10.5555/972764.972768 · Cited by 350
Inside-outside reestimation from partially bracketed corpora
Pereira, F. M. B., & Schabes, Y. (1992) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/981967.981984 · Cited by 292
Inducing Probabilistic Grammars by Bayesian Model Merging
Stolcke, A., & Omohundro, S. (1994) · International Conference on Graphics and Interaction · doi:10.1007/3-540-58473-0_141 · Cited by 288
Cited by
Sorted by citations
Items 601–700 of 789
Vectorial representations of meaning for a computational model of language comprehension
Schuler, W., & Wu, S. (2010) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 2
Automatic Treebank Conversion via Informed Decoding
Zhu, M., & Zhu, J. (2010) · Cited by 2
Learning Chinese language structures with multiple views
Sun, W. (2012) · Publications of the UdS (Saarland University) · doi:10.22028/d291-23607 · Cited by 2
French parsing enhanced with a word clustering method based on a syntactic lexiconPreprint
Sigogne, A., Constant, M., & Laporte, É. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.00634 · Cited by 2
Efficient Parallelization of Natural Language Applications using GPUs
Lai, C.-Y. (2012) · UC Berkeley · Cited by 2
Deep stochastic sentence generation : resources and strategies
Mille, S. (2014) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 2
Resolving pronominal anaphora using commonsense knowledge
Javadpour, S. L. (2013) · doi:10.31390/gradschool_dissertations.1599 · Cited by 2
Web-assisted anaphora resolution
Li, Y. (2010) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3mq6z · Cited by 2
Natural Language Processing Approaches in Bioinformatics
Han, X., & Kwoh, C. K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20463-9 · Cited by 2
Left-corner Methods for Syntactic Modeling with Universal Structural ConstraintsPreprint
能地, 宏. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.00293 · Cited by 2
General methods for fine-grained morphological and syntactic disambiguation
Müller, T. (2015) · doi:10.5282/edoc.18263 · Cited by 2
A survey on 1-bit quantized large language models
Tripathi, K., Malik, D., Akshat, A., & Lata, K. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11529-3 · Cited by 1
Automatic Machine Translation Evaluation with Part-of-Speech Information
Han, A. L.-F., Wong, D. F., Chao, L. S., & He, L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40585-3_16 · Cited by 1
Improved Phrase-based SMT with Syntactic Reordering Patterns Learned from Lattice Scoring.
Jiang, J., Du, J., & Way, A. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 1
Toward Tree Substitution Grammars with Latent Annotations
Ferraro, F., Durme, B. V., & Post, M. (2013) · Cited by 1
Improving Chinese Parsing with Special-Case Probability Re-estimation
Hsieh, Y., Lin, S.-C., Chang, J. S., & Chen, K.-J. (2013) · International Conference on Asian Language Processing · doi:10.1109/ialp.2013.54 · Cited by 1
Random Generation of Nondeterministic Finite-State Tree Automata
Hanneforth, T., Andreas Maletti, A., & Quernheim, D. (2013) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.134.2 · Cited by 1
Hyper-optimization for Deterministic Tree Automata
Andreas Maletti, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39274-0_22 · Cited by 1
Automatic Treebank Conversion via Informed Decoding - A Case Study on Chinese Treebanks
Zhu, M., Zhu, J., & Xiao, T. (2011) · ACM Transactions on Asian Language Information Processing · doi:10.1145/2002980.2002982 · Cited by 1
Detecting targets of alignment moves in multiparty discussions
Marin, A., Wu, W., Zhang, B., & Mari Ostendorf, E. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6289075 · Cited by 1
Nonparametric Bayesian Inference and Efficient Parsing for Tree-adjoining Grammars
Yamangil, E., & Shieber, S. M. (2013) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1
Using Verb-Noun Patterns to Detect Process Inputs
Asadullah, M., Nouvel, D., & Paroubek, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10816-2_23 · Cited by 1
FashionAsk: A Multimedia based Question-Answering System
Zhang, W., Pang, L., & Ngo, C. (2013) · Cited by 1
Broad-Coverage Semantic Dependency Parsing
Oepen, S., Kuhlmann, M., Miyao, Y., Zeman, D., Flickinger, D., Haji, J., Ivanova, A., & Zhang, Y. (2014) · Cited by 1
Parsing German Topological Fields with Probabilistic Context-Free Grammars
Chi, J., & Cheung, K. (2009) · Cited by 1
Building a treebank of noisy user-generated content: The French Social Media Bank
Seddah, D., Sagot, B., Candito, M., Mouilleron, V., & Combet, V. (2012) · HAL · Cited by 1
Learning Better Rule Extraction with Translation Span Alignment
Zhu, J., Xiao, T., & Zhang, C. (2012) · Cited by 1
Fast and Accurate Unlexicalized Parsing via Structural Annotations
Schlund, M., Luttenberger, M., & Esparza, J. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-4032 · Cited by 1
Sigogne, A., Constant, M., Laporte, É., & Sigogne, A. (2011) · HAL · Cited by 1
A Simplified Chinese Parser with Factored Model
Huang, Q., He, L., Wong, D. F., & Chao, L. S. (2012) · Cited by 1
Multiple TreeBanks Integration for Chinese Phrase Structure Grammar Parsing Using Bagging
Zhang, M., Che, W., & Liu, T. (2012) · Cited by 1
Exact Decoding with Multi Bottom-Up Tree Transducers
Quernheim, D. (2015) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w15-3019 · Cited by 1
Oliveira, F. C. M. B., Wong, D. F., Chao, L. S., Liang, T., & He, L. (2013) · International Joint Conference on Natural Language Processing · Cited by 1
Constant, M., Sigogne, A., Watrin, P., & 2012, C. C. J.-T.-R. (2012) · HAL · Cited by 1
SCFG latent annotation for machine translation.
Chung, T., Fang, L., & Gildea, D. (2011) · Cited by 1
Soft String-to-Dependency Hierarchical Machine Translation.
Peter, J.-T., Huck, M., Ney, H., & Stein, D. L. (2011) · Cited by 1
CoMiC: Exploring Text Segmentation and Similarity in the English Entrance Exams Task.
Ziai, R., & Rudzewitz, B. (2015) · CLEF (Working Notes) · Cited by 1
Björklund, J., & Fernau, H. (2016) · Springer eBooks · doi:10.1007/978-3-662-48395-4_7 · Cited by 1
Syntax-Based Pre-reordering for Chinese-to-Japanese Statistical Machine Translation
Han, D., Martínez-Gómez, P., & Miyao, Y. (2016) · Theory and applications of natural language processing · doi:10.1007/978-3-319-21311-8_4 · Cited by 1
A Framework for Discriminative Rule Selection in Hierarchical Moses
Braune, F., Fraser, A., Daumé, H., & Tamchyna, A. (2016) · doi:10.18653/v1/w16-2210 · Cited by 1
Boosting for Efficient Model Selection for Syntactic Parsing
Bawden, R., & Crabbé, B. (2016) · HAL · Cited by 1
Efficient Latent-variable Grammars : Learning and Inference
Dunlop, A. (2014) · doi:10.6083/m42805zt · Cited by 1
A Simple Method for Clarifying Sentences with Coordination Ambiguities
White, M., Duan, M., & King, D. (2017) · doi:10.18653/v1/w17-3702 · Cited by 1
A Treebank for the Healthcare Domain
Oinam, N., Mishra, D., Patel, P., Choudhary, N. K., & Desai, H. (2018) · International Conference on Computational Linguistics · Cited by 1
Research on Chinese-Tibetan Neural Machine Translation
Lai, W., Zhao, X., & Li, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01716-3_9 · Cited by 1
Contributions of Propositional Content and Syntactic Category Information in Sentence Processing
Oh, B.-D., & Schuler, W. (2021) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2021.cmcl-1.28 · Cited by 1
On the Adequacy of Three POS Taggers and a Dependency Parser
Alfared, R., & Béchet, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28604-9_9 · Cited by 1
Unlabeled Dependency Parsing Based Pre-reordering for Chinese-to-Japanese SMT
Han, D., Martínez-Gómez, P., Miyao, Y., Sudoh, K., & Nagata, M. (2014) · Journal of Natural Language Processing · doi:10.5715/jnlp.21.485 · Cited by 1
Syntactic annotation for Portuguese corpora: standards, parsers, and search interfaces
de Faria, P. P. F., Galves, C. M. C., & Magro, C. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09699-4 · Cited by 1
Bayesian Network Automata for Modelling Unbounded Structures
Henderson, J. (2011) · Cited by 1
Bare-Bones Dependency Parsing - A Case for Occam's Razor?
Nivre, J. (2011) · DSpace repository (University of Tartu) · Cited by 1
Improving cross-domain dependency parsing with dependency-derived clusters
Lien, J., Velldal, E., & Øvrelid, L. (2015) · DSpace repository (University of Tartu) · Cited by 1
Semantic Neighborhoods as Hypergraphs
Quirk, C., & Choudhury, P. (2013) · Cited by 1
Joint learning of constituency and dependency grammars by decomposed cross-lingual induction
Jiang, W., Liu, Q., & Supnithi, T. (2015) · International Conference on Artificial Intelligence · Cited by 1
Equivalences between Ranked and Unranked Weighted Tree Automata via Binarization
Dietze, T. (2016) · doi:10.18653/v1/w16-2401 · Cited by 1
Syntactic Parsing Tree in Tibetan Language based on Context Free Grammars
Qiu, L. (2015) · International Journal of Signal Processing Image Processing and Pattern Recognition · doi:10.14257/ijsip.2015.8.11.01 · Cited by 1
A derivational model of discontinuous parsing
Nederhof, M.-J., & Yli-Jyrä, A. (2020) · Information and Computation · doi:10.1016/j.ic.2020.104619 · Cited by 1
Yang, L., Sun, M., Cheng, Y., Zhang, J.-C., Liu, Z., Luan, H., & Liu, Y. (2017) · Journal of Computer Science and Technology · doi:10.1007/s11390-017-1756-5 · Cited by 1
Progress of research in machine learning-based protein-protein interaction extraction
Jie, M. A., YunPing, Z. H. U., Li, M., & Chang, C. K. (2016) · Scientia Sinica Vitae · doi:10.1360/n052016-00142 · Cited by 1
Imamura, K., & Sumita, E. (2016) · International Conference on Computational Linguistics · Cited by 1
Translation systems and experimental results of the EHR group for WAT2016 tasks
Ehara, T. (2016) · International Conference on Computational Linguistics · Cited by 1
The SAS Statistical Machine Translation System for WAT 2014
Wang, R., Xu, Y., & Gao, Y. (2014) · Cited by 1
Incorporating target language semantic roles into a string-to-tree translation model
Su, C., Guo, Y., Huang, H., Shi, S., & Feng, C. (2017) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1601349 · Cited by 1
One-Counter Automata for Parsing and Language Approximation
Sakharov, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94812-6_25 · Cited by 1
Teranishi, H., Shindo, H., & Matsumoto, Y. (2018) · Journal of Natural Language Processing · doi:10.5715/jnlp.25.441 · Cited by 1
Approach to Automatic Determining of Speakers of Direct Speech Fragments in Natural Language Texts
Александрович, С. О., Kamennov, Y., & Shurlaeva, E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-25719-4_68 · Cited by 1
Coordinate Structure Analysis using Local Models and CKY Algorithm
Teranishi, H., Shindo, H., Watanabe, T., & Matsumoto, Y. (2020) · Journal of Natural Language Processing · doi:10.5715/jnlp.27.719 · Cited by 1
Neural Combinatory Constituency Parsing
Chen, Z., Zhang, L., Imankulova, A., & Komachi, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.194 · Cited by 1
Poesio, M., Chamberlain, J., & Kruschwitz, U. (2017) · Springer eBooks · doi:10.1007/978-94-024-0881-2_43 · Cited by 1
Björklund, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43587-4_6 · Cited by 1
Jumelet, J., & Zuidema, W. H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.288 · Cited by 1
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
Opper, M., Prokhorov, V., & Siddharth, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.469 · Cited by 2
Proceedings of the Third Workshop on Annotation of Corpora for Research in the Humanities (ACRH-3)
Mambrini, F., Passarotti, M., & Sporleder, C. (2013) · Cited by 1
Soft features for statistical machine translation of spoken and signed languages
Stein, D. L., & Ney, H. (2012) · RWTH Publications (RWTH Aachen) · Cited by 1
Complete Search Space Exploration for SITG Inside Probability
Gascó, G., Sánchez, J. A., & Benedí, J.-M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14980-1_29 · Cited by 1
Conversion between dependency structures and phrase structures using a head finder algorithm
Li, X., Wang, X., & Yao, L. (2010) · International Conference on Natural Language Processing and Knowledge Engineering (NLPKE) · doi:10.1109/nlpke.2010.5587792 · Cited by 1
Integrated Supertagging and Parsing
Auli, M. (2012) · ERA · Cited by 1
Improved Evaluation and Parse Reranking for Combinatory Categorial Grammar
Ng, D. (2010) · Cited by 1
Using Dependency Parses to Augment Feature Construction for Text Mining
Guo, S. H. (2012) · VTechWorks (Virginia Tech) · Cited by 1
Semantic representation learning for discourse processing
Ji, Y. (2016) · SMARTech Repository (Georgia Institute of Technology) · Cited by 1
Minimally supervised dependency-based methods for natural language processing
Rei, M. (2021) · CL Technical Reports · doi:10.48456/tr-840 · Cited by 1
Incremental generative models for syntactic and semantic natural language processing
Buys, J. (2017) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-r5dkwkykv · Cited by 1
Unsupervised models of entity reference resolution
Klein, D., & Haghighi, A. (2010) · Cited by 1
Self-citationBawden, R., Zhang, B., Yankovskaya, L., Tättar, A., & Post, M. (2020) · arXiv (Cornell University) · Cited by 1
Recognising Biomedical Names: Challenges and SolutionsPreprint
Dai, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12230 · Cited by 3
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language ModelsPreprint
Kim, T., Li, B., & Lee, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13805 · Cited by 1
Phrase extraction and rescoring in statistical machine translation
Srivastava, A. K. (2014) · Arrow@dit (Dublin Institute of Technology) · Cited by 1
Discontinuous constituency parsing of morphologically rich languages
Coavoux, M. (2017) · theses.fr (ABES) · doi:10.70675/8bb6891eza6f9z4466z8da9zfab12af43750 · Cited by 1
Collins-LA: Collins' Head-Driven Model with Latent Annotation
Na, S., Jin, M., Kang, I.-S., & Lee, J.-H. (2008)
Vogel, S., & Venugopal, A. (2009)
Using subcategorization frames to improve French probabilistic parsing
Sigogne, A., & Constant, M. (2012) · HAL
Phrasal Syntactic Category Sequence Model for Phrase-Based MT
Cao, H., Sumita, E., Zhao, T., & Li, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28601-8_5
Decomposing and regenerating syntactic trees
Sangati, F. (2012) · UvA-DARE (University of Amsterdam)
Encoding structured output values
Palmer, M. S., & Loper, E. (2008)
Brew, C., White, M., & Boxwell, S. A. (2011) · ProQuest LLC eBooks
Hyper-Minimization for Deterministic Weighted Tree Automata
Andreas Maletti, A., & Quernheim, D. (2014) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.151.22
Hyper-optimization for deterministic tree automata
Andreas Maletti, A. (2015) · Theoretical Computer Science · doi:10.1016/j.tcs.2015.01.020
Metadata
- 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 ›
Are you an author, or do you see a problem with this page? Request a review