Learning accurate, compact, and interpretable tree annotation

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

Head-Driven Statistical Models for Natural Language Parsing

Collins, M. (2003) · Computational Linguistics · doi:10.1162/089120103322753356 · Cited by 1,822

Untitled work

(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

Exploiting prosody for PCFGs with latent annotations

Dreyer, M., & Izhak Shafran, J. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-216 · Cited by 13

Identification of Pleonastic It Using the Web

Li, Y., Musilek, P., Reformat, M., & Wyard-Scott, L. (2009) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2622 · Cited by 13

Automatic Parallel Fragment Extraction from Noisy Data

Riesa, J., & Marcu, D. (2012) · Cited by 12

Kernel-Based Semantic Relation Detection and Classification via Enriched Parse Tree Structure

周国栋, & 朱巧明 (2011) · Journal of Computer Science and Technology · doi:10.1007/s11390-011-1110-2 · Cited by 12

Can Translation Memories afford not to use paraphrasing

Gupta, R., Orǎsan, C., Zampieri, M., Vela, M., & van Genabith, J. (2015) · Cited by 12

How Does the Granularity of an Annotation Scheme Influence Dependency Parsing Performance

Mille, S., Burga, A., Ferraro, G., & Wanner, L. (2012) · International Conference on Computational Linguistics · Cited by 12

Edinburgh’s Syntax-Based Systems at WMT 2014

Williams, P., Sennrich, R., Nădejde, M., Huck, M., Hasler, E., & Koehn, P. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3324 · Cited by 12

Parser Evaluation and the BNC: Evaluating 4 constituency parsers with 3 metrics

Foster, J., & van Genabith, J. (2008) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2kobxa2925pm · Cited by 12

Querying parse trees of stochastic context-free grammars

Cohen, S., & Kimelfeld, B. (2010) · International Conference on Database Theory · doi:10.1145/1804669.1804680 · Cited by 12

Modifying Corpus Annotation to Support the Analysis of Learner Language

Dickinson, M., & Lee, C. (2009) · CALICO Journal · doi:10.1558/cj.v26i3.545-561 · Cited by 12

Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

Barone, A. V. M., & Attardi, G. (2015) · doi:10.3115/v1/w15-1002 · Cited by 12

Survey: Finite-state technology in natural language processing

Andreas Maletti, A. (2016) · Theoretical Computer Science · doi:10.1016/j.tcs.2016.05.030 · Cited by 12

Syntax-based Statistical Machine Translation

Williams, P. J., Sennrich, R., Post, M., & Koehn, P. (2016) · Synthesis lectures on human language technologies · doi:10.2200/s00716ed1v04y201604hlt033 · Cited by 12

Coordination Boundary Identification with Similarity and Replaceability

Teranishi, H., Shindo, H., & Matsumoto, Y. (2017) · International Joint Conference on Natural Language Processing · Cited by 12

A two-level deep learning approach for emotion recognition in Arabic news headlines

Elsayed, H. A. G., Chaffar, S., Belhouari, S. B., & Raissouli, H. (2020) · International Journal of Computers and Applications · doi:10.1080/1206212x.2020.1851501 · Cited by 12

On the role of lexical features in sequence labeling

Goldberg, Y., & Elhadad, M. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699648.1699660 · Cited by 12

Learning Structured Models for Phone Recognition

Petrov, S., Pauls, A., & Klein, D. (2007) · Cited by 12

Syntax-based language models for statistical machine translation

Gildea, D., & Post, M. J. (2010) · Cited by 12

Morphological Features for Parsing Morphologically-rich Languages: A Case of Arabic

Dehdari, J., Tounsi, L., & van Genabith, J. (2011) · Cited by 11

Relaxed Cross-lingual Projection of Constituent Syntax

Jiang, W., Liu, Q., & Lv, Y. (2011) · Cited by 11

Diversity in Spectral Learning for Natural Language Parsing

Narayan, S., & Cohen, S. B. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1214 · Cited by 11

Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation

Goto, I., Utiyama, M., & Sumita, E. (2013) · ACM Transactions on Asian Language Information Processing · doi:10.1145/2518100 · Cited by 11

Extracting Clinical Relations in Electronic Health Records Using Enriched Parse Trees

Kim, J., Choe, Y., & Mueller, K. D. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.304 · Cited by 11

Special Techniques for Constituent Parsing of Morphologically Rich Languages

Szántó, Z., & Farkas, R. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1015 · Cited by 11

Subsentential Sentiment on a Shoestring: A Crosslingual Analysis of Compositional Classification

Haas, M., & Versley, Y. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1071 · Cited by 11

Choosing the best machine translation system to translate a sentence by using only source-language information

Sánchez-Martínez, F. (2011) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · Cited by 11

Forest-based algorithms in natural language processing

Joshi, A. K., Knight, K. K., & Huang, L. (2008) · Cited by 11

Statistical Parsing of Spanish and Data Driven Lemmatization

Roux, J. L., Sagot, B., & Seddah, D. (2012) · HAL · Cited by 11

Frequency Effects in the Processing of Unbounded Dependencies

van Schijndel, M., Schuler, W., & Culicover, P. W. (2014) · eScholarship (California Digital Library) · Cited by 11

IMS HotCoref DE: A Data-driven Co-reference Resolver for German

Rösiger, I., & Kuhn, J. (2016) · Language Resources and Evaluation · Cited by 11

Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface FeaturesPreprint

Rutherford, A., Demberg, V., & Xue, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01990 · Cited by 11

Stacking of Dependency and Phrase Structure Parsers

Farkas, R., & Bohnet, B. (2012) · Cited by 10

Generic refinement of expressive grammar formalisms with an application to discontinuous constituent parsing.

Gebhardt, K. (2018) · International Conference on Computational Linguistics · Cited by 10

Improving translation memory fuzzy matching by paraphrasing

Chatzitheodorou, K. (2015) · Cited by 10

Handling Ambiguities of Bilingual Predicate-Argument Structures for Statistical Machine Translation

Zhai, F., Zhang, J., Yu, Z., & Zong, C. (2013) · Cited by 10

Parsing low-resource languages using Gibbs sampling for PCFGs with latent annotations

Sun, L., Mielens, J. D., & Baldridge, J. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1035 · Cited by 10

Tree-based Translation without using Parse Trees

Zhai, F., Zhang, J., Zhou, Y., & Zong, C. (2012) · Cited by 10

Constructing a Practical Constituent Parser from a Japanese Treebank with Function Labels

Tanaka, T., & Nagata, M. (2013) · doi:10.18653/v1/w13-4913 · Cited by 10

Because Syntax Does Matter: Improving Predicate-Argument Structures Parsing with Syntactic Features

Ribeyre, C., de la Clergerie, É. V., & Seddah, D. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1007 · Cited by 10

Parsing-based Chinese word segmentation integrating morphological and syntactic information

Wu, X. H., Zhang, M., & Lin, X. (2011) · International Conference on Natural Language Processing and Knowledge Engineering (NLPKE) · doi:10.1109/nlpke.2011.6138178 · Cited by 10

Improving translation memory matching and retrieval using paraphrases

Gupta, R., Orǎsan, C., Zampieri, M., Vela, M., van Genabith, J., & Mitkov, R. (2016) · Machine Translation · doi:10.1007/s10590-016-9180-0 · Cited by 10

Hybrid Grammars for Parsing of Discontinuous Phrase Structures and Non-Projective Dependency Structures

Gebhardt, K., Nederhof, M.-J., & Vogler, H. (2017) · Computational Linguistics · doi:10.1162/coli_a_00291 · Cited by 10

Syntactic Analysis of the Tunisian Arabic.

Mekki, A., Zribi, I., Ellouze, M., & Belguith, L. H. (2017) · Cited by 10

Decomposed Local Models for Coordinate Structure Parsing

Teranishi, H., Shindo, H., & Matsumoto, Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1343 · Cited by 10

Machine Learning for Natural Language Processing

Emms, M. (2011) · Cited by 10

Unlexicalised hidden variable models of split dependency grammars

Musillo, G., & Merlo, P. (2008) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1557690.1557751 · Cited by 10

Reordering Grammar Induction

Stanojević, M., & Sima’an, K. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1005 · Cited by 10

Hard Constraints for Grammatical Function Labelling

Seeker, W., Rehbein, I., Kuhn, J., & van Genabith, J. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 10

Rich statistical parsing and literary language

van Cranenburgh, A. (2012) · UvA-DARE (University of Amsterdam) · Cited by 10

Top-Down K-Best A* Parsing

Pauls, A., Klein, D., & Quirk, C. (2010) · Cited by 9

Locality and expectation effects in Hindi preverbal constituent ordering

Ranjan, S., Rajkumar, R. P., & Agarwal, S. (2022) · Cognition · doi:10.1016/j.cognition.2021.104959 · Cited by 9

Decreasing Lexical Data Sparsity in Statistical Syntactic Parsing - Experiments with Named Entities

Hogan, D., Foster, J., & van Genabith, J. (2011) · Arrow@dit (Dublin Institute of Technology) · Cited by 9

Lexicon expansion for latent variable grammars

Zeng, X., Wong, D. F., Chao, L. S., Trancoso, I. M., He, L., & Huang, Q. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.01.010 · Cited by 9

Augmenting String-to-Tree and Tree-to-String Translation with Non-Syntactic Phrases

Huck, M., Hoang, H., & Koehn, P. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3362 · Cited by 9

Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing

Sun, W., & Wan, X. (2013) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00229 · Cited by 9

Parsing Paraphrases with Joint Inference

Choe, D. K., & McClosky, D. (2015) · doi:10.3115/v1/p15-1118 · Cited by 9

An Entity-Level Approach to Information Extraction

Haghighi, A., & Klein, D. (2010) · Cited by 9

Parse Reranking Based on Higher-Order Lexical Dependencies

Wang, Z., & Zong, C. (2011) · Cited by 9

Parse Correction with Specialized Models for Difficult Attachment Types

Anguiano, E. H., & Candito, M. (2011) · Cited by 9

Dependency Link Embeddings: Continuous Representations of Syntactic Substructures

Bansal, M. (2015) · Workshop on Vector Space Modeling for Natural Language Processing (VSM-NLP) · doi:10.3115/v1/w15-1514 · Cited by 9

Data point selection for self-training

Rehbein, I. (2011) · Cited by 9

An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing

van Schijndel, M., Nguyen, L. V., & Schuler, W. (2013) · Cited by 9

Feature-Rich Log-Linear Lexical Model for Latent Variable PCFG Grammars

Huang, Z., & Harper, M. P. (2011) · International Joint Conference on Natural Language Processing · Cited by 9

Bayesian Analysis in Natural Language Processing

Cohen, S. B. (2019) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02170-1 · Cited by 9

On different approaches to syntactic analysis into bi-lexical dependencies: An empirical comparison of direct, PCFG-based, and HPSG-based parsers

Ivanova, A., Oepen, S., Dridan, R., Flickinger, D., Øvrelid, L., & Lapponi, E. (2016) · Journal of Language Modelling · doi:10.15398/jlm.v4i1.101 · Cited by 9

Top-Down Nearly-Context-Sensitive Parsing

Eugene Charniak, D. (2010) · Cited by 9

Accurate Maximum-Margin Training for Parsing With Context-Free Grammars

Bauer, A., Braun, M. L., & Müller, K. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2497149 · Cited by 9

Forest-to-String SMT for Asian Language Translation: NAIST at WAT 2014

Neubig, G. (2014) · Cited by 9

Language Technologies for the Challenges of the Digital Age

Rehm, G., & Declerck, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73706-5 · Cited by 10

Collapsed Variational Bayesian Inference for PCFGs

Wang, P., & Blunsom, P. (2013) · Cited by 9

Analysis of Experiments on Statistical and Neural Parsing for a Morphologically Rich and Free Word Order Language Urdu

Ehsan, T., & Hussain, S. (2019) · IEEE Access · doi:10.1109/access.2019.2949950 · Cited by 9

Detecting grammatical errors with treebank-induced, probabilistic parsers

Wagner, J. (2012) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 9

Proceedings of the Second Workshop on Discourse in Machine Translation

(2015) · doi:10.18653/v1/w15-25 · Cited by 9

A Bayesian Model for Generative Transition-based Dependency ParsingPreprint

Buys, J., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.04334 · Cited by 9

Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic InformationPreprint

Tran, T., Toshniwal, S., Bansal, M., Gimpel, K., Livescu, K., & Mari Ostendorf, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07287 · Cited by 17

Correcting real-word spelling errors: A new hybrid approach

Dashti, S. M. S., Bardsiri, A. K., & Bardsiri, V. K. (2017) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqx054 · Cited by 8

Multilingual discriminative shift reduce phrase structure parsing for the SPMRL 2014 shared task

Crabbé, B., & Seddah, D. (2014) · HAL · Cited by 8

Filtering with Abstract Particles

Steinhardt, J., & Liang, P. (2014) · Cited by 8

Training Factored PCFGs with Expectation Propagation

Hall, D., & Klein, D. (2012) · Cited by 8

Spectral Learning of Refinement HMMs

Stratos, K., Rush, A. M., Cohen, S. B., & Collins, M. (2013) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 8

Predicting the functions of a protein from its ability to associate with other molecules

Taha, K., & Yoo, P. D. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0882-3 · Cited by 8

Syntactic Parse Fusion

Choe, D. K., McClosky, D., & Eugene Charniak, D. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1160 · Cited by 8

Quality Estimation of English-French Machine Translation: A Detailed Study of the Role of Syntax

Kaljahi, R., Foster, J., Roturier, J., & Rubino, R. (2014) · International Conference on Computational Linguistics · Cited by 8

Modeling of learning curves with applications to POS tagging

Ferro, M. V., Bilbao, V. M. D., & Ribadas, F. J. (2016) · Computer Speech & Language · doi:10.1016/j.csl.2016.06.001 · Cited by 8

Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

Barone, A. V. M., & Attardi, G. (2015) · doi:10.3115/v1/p15-1082 · Cited by 8

Towards Replicability in Parsing

Dakota, D., & Kübler, S. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_026 · Cited by 8

Towards Semantic Approaches for General-Purpose End-User Development

Atzeni, M., & Atzori, M. (2018) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2018.00077 · Cited by 8

Simple but Effective Approaches to Improving Tree-to-tree Model

Zhai, F., Zhang, J., Yu, Z., & Zong, C. (2011) · Cited by 8

Improving PCFG Chinese Parsing with Context-Dependent Probability Re-estimation

Hsieh, Y., Bai, M.-H., Chang, J. S., & Chen, K.-J. (2012) · Cited by 8

Structured composition of semantic vectors

Wu, S., & Schuler, W. (2011) · Cited by 8

An LR-inspired generalized lexicalized phrase structure parser

Crabbé, B., & Ricoeur, P. P. (2014) · Cited by 8

Bilingually-Guided Monolingual Dependency Grammar Induction

Liu, K., Lü, Y., Jiang, W., & Liu, Q. (2013) · Cited by 8

Hard Time Parsing Questions: Building a QuestionBank for French

Seddah, D., & Candito, M. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3iyxej6r6ug3 · Cited by 8

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

(2015) · doi:10.18653/v1/w15-26 · Cited by 8

Refining grammars for parsing with hierarchical semantic knowledge

Lin, X., Yang, F., Zhang, M., Wu, X. H., & Chi, H. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699648.1699676 · Cited by 7

Feature-Rich Phrase-based Translation: Stanford University's Submission to the WMT 2013 Translation Task

Green, S., Cer, D. M., Reschke, K. S., Voigt, R., Bauer, J., Wang, S. I., Silveira, N., Neidert, J., & Manning, C. D. (2013) · Workshop on Statistical Machine Translation · Cited by 7

Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation

Huck, M., Hoang, H., & Koehn, P. (2014) · doi:10.3115/v1/w14-4018 · Cited by 7

Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation

Han, A. L.-F., Wong, D. F., Chao, L. S., He, L., & Lu, Y. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/760301 · Cited by 7

UW-MRS: Leveraging a Deep Grammar for Robotic Spatial Commands

Packard, W. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2144 · Cited by 7

Using unlabeled dependency parsing for pre-reordering for Chinese-to-Japanese statistical machine translation

Han, D., Martínez-Gómez, P., Miyao, Y., Sudoh, K., & Nagata, M. (2013) · Cited by 7

Toward Tree Substitution Grammars with Latent Annotations

Ferraro, F., Durme, B. V., & Post, M. (2013) · Cited by 1

Abstract

(2008)

Tree Annotation Tool using Two-phase Parsing to Reduce Manual Effort for Building a Treebank

Park, S., Cho, Y., Son, S., Song, U.-S., & Rim, H. (2005) · Cited by 2

Proceedings of the LFG05 Conference

Burke, M., Cahill, A., Genabith, J. V., Way, A., Butt, M., & (editors, T. H. K. (2005)

Automation of treebank annotation

Brants, T., & Skut, W. (1998) · Conference on Computational Natural Language Learning · doi:10.3115/1603899.1603909 · Cited by 34