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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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“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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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
Flagged first, then by citations
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Items 501–600 of 789
An information-theoretic measure to evaluate parsing difficulty across treebanks
Corazza, A., Lavelli, A., & Satta, G. (2013) · ACM Transactions on Speech and Language Processing · doi:10.1145/2407736.2407737 · Cited by 4
Effective Constituent Projection across Languages
Jiang, W., Lv, Y., Liu, Y., & Liu, Q. (2010) · Cited by 4
Learning from errors: Using vector-based compositional semantics for parse reranking
Le, P., Zuidema, W. H., Scha, R. J. H., Allauzen, A., Larochelle, H., Manning, C. D., & Socher, R. (2013) · UvA-DARE (University of Amsterdam) · Cited by 4
Sun, W., Peng, X., & Wan, X. (2015) · Cited by 4
Parsing Any Domain English text to CoNLL dependencies
Kolachina, S., & Kolachina, P. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4i5w8a8kjs4c · Cited by 4
Left-corner Parsing for Dependency Grammar
Noji, H., & Miyao, Y. (2015) · Journal of Natural Language Processing · doi:10.5715/jnlp.22.251 · Cited by 4
An Improved CRF based Chinese Language Processing System for SIGHAN Bakeoff 2007.
Wu, X. H., Lin, X., Wang, X., Wu, C., Zhang, Y.-Z., & Yu, D. (2008) · International Joint Conference on Natural Language Processing · Cited by 4
Lin, Z. J., Duan, Y., Zhao, Y., Sun, W., & Wan, X. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1414 · Cited by 4
Reified Context ModelsPreprint
Steinhardt, J., & Liang, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.06665 · Cited by 4
Statistical Parsing of Noun Phrase Structure
Vadas, D. (2010) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 4
Unsupervised Bayesian Lexicalized Dependency Grammar Induction
Headden, W. P. (2012) · doi:10.7301/z0n29v7j · Cited by 4
Non-Deterministic Segmentation for Chinese Lattice Parsing
Hu, H., Dakota, D., & Kübler, S. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_043 · Cited by 3
Syntax and Parsing of Semitic Languages
Tsarfaty, R. (2014) · Theory and applications of natural language processing · doi:10.1007/978-3-642-45358-8_3 · Cited by 3
Chinese Unknown Word Recognition for PCFG-LA Parsing
Huang, Q., He, L., Wong, D. F., & Chao, L. S. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/959328 · Cited by 3
A Study on Recursive Neural Network Based Sentiment Classification of Sina Weibo
Fu, C., Xue, B., & Shaobin, Z. (2014) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2014.88 · Cited by 3
An Exploration of Forest-to-String Translation: Does Translation Help or Hurt Parsing?
Zhang, H., & Chiang, D. (2012) · Cited by 3
Dependency parsing with latent refinements of part-of-speech tags
Mueller, T., Farkas, R., Judea, A., Schmid, H., & Schuetze, H. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1103 · Cited by 3
Large-scale Exact Decoding: The IMS-TTT submission to WMT14
Quernheim, D., & Cap, F. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3318 · Cited by 3
Parallel CYK Membership Test on GPUs
Kim, K.-H., Choi, S.-M., Lee, H., Man, K. L., & Han, Y.-S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44917-2_14 · Cited by 3
Roux, J. L., Favre, B., Nasr, A., & Mirroshandel, S. A. (2012) · HAL · Cited by 3
Unsupervised Tree Induction for Tree-based Translation
Zhai, F., Zhang, J., Zhou, Y., & Zong, C. (2013) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00224 · Cited by 3
Treebank Parsing and Knowledge of Language
Fong, S., Malioutov, I., Yankama, B., & Berwick, R. C. (2012) · Theory and applications of natural language processing · doi:10.1007/978-3-642-31863-4_6 · Cited by 3
Ambiguity Resolution for Vt-N Structures in Chinese
Hsieh, Y., Chang, J. S., & Chen, K.-J. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1100 · Cited by 3
Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing
Zhang, M., Che, W., Liu, T., & Li, Z. (2012) · Cited by 3
Transition-based dependency parsing as latent-variable constituent parsing
Nederhof, M.-J. (2016) · doi:10.18653/v1/w16-2403 · Cited by 3
Berkling, K., Lavalley, R., & Reichel, U. D. (2015) · Cited by 3
Patent Claim Translation based on Sublanguage-specific Sentence Structure
Fuji, M., Fujita, A., Utiyama, M., Sumita, E., & Matsumoto, Y. (2016) · Journal of Natural Language Processing · doi:10.5715/jnlp.23.407 · Cited by 3
System Combination of RBMT plus SPE and Preordering plus SMT.
Ehara, T. (2015) · Cited by 3
Combining Machine Translated Sentence Chunks from Multiple MT Systems
Rikters, M., & Skadiņa, I. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75487-1_3 · Cited by 3
All Fragments Count in Parser Evaluation
Bastings, J., & Sima’an, K. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3mtmu6wjvvpu · Cited by 3
An Empirical Evaluation of Automatic Conversion from Constituency to Dependency in Hungarian
Simkó, K. I., Vincze, V., Szántó, Z., & Farkas, R. (2014) · SZTE Publicatio Repozitórium (University of Szeged) · Cited by 3
POS Tagset Refinement for Linguistic Analysis and the Impact on Statistical Parsing
Rehbein, I., & Hirschmann, H. (2023) · Publication Server of the Institute for German Language (Institute for German Language) · doi:10.5281/zenodo.10055048 · Cited by 3
Toward a Multi-Representation Persian Treebank
Dehghan, M. H., Molla-Abbasi, M., & Faili, H. (2018) · International Symposium on Telecommunications · doi:10.1109/istel.2018.8660996 · Cited by 3
Treebank‐Based Probabilistic Phrase Structure Parsing
Cahill, A. (2007) · Language and Linguistics Compass · doi:10.1111/j.1749-818x.2007.00046.x · Cited by 3
Improving Dependency Parsing with Fined-Grained Features
Zhou, G., Cai, L., Liu, K., & Zhao, J. (2013) · Cited by 3
Data Acquisition and Linguistic Resources
Strassel, S. M., Christianson, C., McCary, J., Staderman, W., & Olive, J. P. (2011) · Springer eBooks · doi:10.1007/978-1-4419-7713-7_1 · Cited by 3
Heterogeneous Parsing via Collaborative Decoding
Zhu, M., Zhu, J., & Xiao, T. (2010) · Cited by 3
Iterative Transformation of Annotation Guidelines for Constituency Parsing
Li, X., Jiang, W., Lü, Y., & Liu, Q. (2013) · Cited by 3
Combining discriminative re-ranking and co-training for parsing Mandarin speech transcripts
Wang, W. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4960681 · Cited by 3
Improving Part-of-speech Tagging for Context-free Parsing
Chen, X. D., & Kit, C. (2011) · CityU Scholars · Cited by 3
Automatic Extraction and Evaluation of Arabic LFG Resources
Attia, M. A., Shaalan, K. F., Tounsi, L., & van Genabith, J. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3pmha53egzrc · Cited by 3
Parsing Chinese with a Generalized Categorial Grammar
Duan, M., & Schuler, W. (2015) · doi:10.18653/v1/w15-3304 · Cited by 3
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Anguiano, E. H., & Candito, M. (2012) · Cited by 3
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Seddah, D., Candito, M., Crabbé, B., & Anguiano, E. H. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/42ce86ehimsy · Cited by 3
Ingason, A. K., Loftsson, H., Rögnvaldsson, E., Sigurðsson, E. F., & Wallenberg, J. C. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/55gbszv9v5qd · Cited by 3
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Minkov, E. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2801131 · Cited by 3
Towards a syntactically motivated analysis of modifiers in German.
Rehbein, I., & Hirschmann, H. (2014) · HilDok – Institutional Repository (Universität Hildesheim) · Cited by 3
Lavalley, R., & Berkling, K. (2014) · HilDok – Institutional Repository (Universität Hildesheim) · Cited by 3
K-Translate - Interactive Multi-system Machine Translation
Rikters, M. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-40180-5_21 · Cited by 3
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Ribeyre, C., de la Clergerie, É. V., & Seddah, D. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3qwi5kfcfpnu · Cited by 3
KyotoEBMT System Description for the 1st Workshop on Asian Translation.
Richardson, J. T. E., Dabre, R., Chu, C., Cromierès, F., Nakazawa, T., & Kurohashi, S. (2014) · Cited by 3
Weblio Pre-reordering Statistical Machine Translation System
Zhu, Z. (2014) · Cited by 3
Robust handling of out-of-vocabulary words in deep language processing
Silva, J. (2014) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 3
Samad, R., & Kaljahi, Z. (2015) · Arrow@dit (Dublin Institute of Technology) · Cited by 3
Transformation and Combination in Data-Driven Dependency Parcing
Nilsson, J. (2009) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 3
Word Representations, Tree Models and Syntactic FunctionsPreprint
Šuster, S., Noord, V. G., & Titov, I. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.07709 · Cited by 3
Etude de la paraphrase sous-phrastique en traitement automatique des langues
Bouamor, H. (2012) · theses.fr (ABES) · doi:10.70675/8f510473zcf4cz4090z953az3d3b77284693 · Cited by 3
Un treebank pour le serbe : constitution et exploitations
Miletić, A. (2018) · theses.fr (ABES) · doi:10.70675/3c22de79z17c0z4f4dz8d15z07bbebb4c96b · Cited by 3
Stuart, L. M., Taylor, J. M., & Raskin, V. (2012) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2012.6378029 · Cited by 2
Augmented Parsing of Unknown Word by Graph-Based Semi-Supervised Learning
Huang, Q., Wong, D. F., Chao, L. S., Zeng, X., & He, L. (2013) · Institutional Repositories DataBase (IRDB) · Cited by 2
Adapting Multilingual Parsing Models to Sinica Treebank
He, L., Wong, D. F., & Chao, L. S. (2012) · Cited by 2
Zhang, Y., Oepen, S., Dridan, R., Flickinger, D., & Krieger, H. (2014) · Cited by 2
Saarland University’s Participation in the GErman SenTiment AnaLysis shared Task (GESTALT)
Wiegand, M., Bocionek, C., Conrad, A., Dembowski, J., Giesen, J., Linn, G., & Schmeling, L. (2014) · HilDok – Institutional Repository (Universität Hildesheim) · Cited by 2
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Nederhof, M.-J., & Yli-Jyrä, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-53733-7_22 · Cited by 2
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Seemann, N., Braune, F., & Andreas Maletti, A. (2015) · Cited by 2
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Zhang, L., & Chan, K.-P. (2012) · ACM Transactions on Asian Language Information Processing · doi:10.1145/2334801.2334803 · Cited by 2
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Büchse, M., & Fischer, A. (2012) · Cited by 2
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Park, J., & Kim, M. (2024) · Korean Linguistics · doi:10.1075/kl.00008.par · Cited by 2
Nivre, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-25261-7_2 · Cited by 2
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Jamison, E. (2011) · Cited by 2
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Pate, J. K., & Meurers, D. (2007) · Cited by 2
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Kovacs, G., & Miller, R. C. (2013) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2508468.2514931 · Cited by 2
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Lin, X., Zhang, M., & Wu, X. H. (2010) · International Conference on Electrical and Control Engineering · doi:10.1109/icece.2010.149 · Cited by 2
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Germann, U. (2012) · Cited by 2
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Bansal, M., & Klein, D. (2011) · Cited by 2
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Guillou, L., & Webber, B. (2015) · Workshop on Discourse in Machine Translation (DiscoMT) · doi:10.18653/v1/w15-2503 · Cited by 2
Avramidis, E., & Popović, M. (2013) · Workshop on Statistical Machine Translation · Cited by 2
Rule Selection with Soft Syntactic Features for String-to-Tree Statistical Machine Translation
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Hangya, V., Szántó, Z., & Farkas, R. (2017) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/ciapp.2017.8167217 · Cited by 2
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Li, D., Zhang, X., & Wu, X. H. (2013) · International Workshop/Conference on Parsing Technologies · Cited by 2
Gildea, D. (2020) · Computational Linguistics · doi:10.1162/coli_a_00386 · Cited by 2
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Ortmann, K. (2020) · Cited by 2
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Razavi, M. (2012) · Summit (Simon Fraser University) · Cited by 2
Subcategorisation Acquisition from Raw Text for a Free Word-Order Language
Roberts, W., Egg, M., & Kordoni, V. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1032 · Cited by 2
Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation
Rikters, M. (2016) · Workshop on Hybrid Approaches to Translation · Cited by 2
Bayesian Analysis in Natural Language Processing, Second Edition
Cohen, S. B. (2019) · Synthesis lectures on human language technologies · doi:10.2200/s00905ed2v01y201903hlt041 · Cited by 3
Triandafil, C. M., Brezeanu, P. M., & Badea, L. (2008) · RePEc: Research Papers in Economics · Cited by 2
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
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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.
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