The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
Multilingual dependency analysis with a two-stage discriminative parser
Open accessAbstract
We present a two-stage multilingual dependency parser and evaluate it on 13 diverse languages. The first stage is based on the unlabeled dependency parsing models described by McDonald and Pereira (2006) augmented with morphological features for a subset of the languages. The second stage takes the output from the first and labels all the edges in the dependency graph with appropriate syntactic categories using a globally trained sequence classifier over components of the graph. We report results on the CoNLL-X shared task (Buchholz et al., 2006) data sets and present an error analysis.
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
“The average length of Arabic sentence is widely higher than other languages, it is around 35 words per sentence [38].”
From 2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents (2018) · cites this paper as [38]
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
Dependency Syntax: Theory and Practice
Mel’čuk, I. (1987) · Medical Entomology and Zoology · Cited by 1,417
CoNLL-X shared task on multilingual dependency parsing
Buchholz, S., & Marsi, E. (2006) · Conference on Computational Natural Language Learning · doi:10.3115/1596276.1596305 · Cited by 961
Non-projective dependency parsing using spanning tree algorithms
McDonald, R., Pereira, F. M. B., Ribarov, K., & Hajič, J. (2005) · doi:10.3115/1220575.1220641 · Cited by 851
Self-citationOnline large-margin training of dependency parsers
McDonald, R., Crammer, K., & Pereira, F. M. B. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219852 · Cited by 795
Self-citationOnline Learning of Approximate Dependency Parsing Algorithms.
McDonald, R., & Pereira, F. C. N. (2006) · Cited by 465
Self-citationOflazer, K., Say, B., Hakkani-Tür, D. Z., & Tür, G. (2003) · Text, speech and language technology · doi:10.1007/978-94-010-0201-1_15 · Cited by 233
Ultraconservative Online Algorithms for Multiclass Problems
Crammer, K., & Singer, Y. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_7 · Cited by 221
Treebanks Building and Using Parsed Corpora
Abeillé, A. (2014) · Cited by 217
Machine translation using probabilistic synchronous dependency insertion grammars
Ding, Y., & Palmer, M. S. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219907 · Cited by 199
The Alpino Dependency Treebank
van der Beek, L., Bouma, G., Malouf, R., & van Noord, G. (2002) · BRILL eBooks · doi:10.1163/9789004334038_003 · Cited by 175
Research works printed but not linked: 1
- R. McDonald. 2006. Discriminative sentence compres- sion with soft syntactic constraints. In Proc. EACL.
Cited by
Sorted by citations
Items 201–223 of 223
Khmylko, L., & Menzel, W. (2010) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-642-10745-0_72
Mining and modelling interaction networks for systems biology
Fayruzov, T. (2010) · Ghent University Academic Bibliography (Ghent University)
Research on Semantic Disambiguation in Treebank
Miao, L., Lv, X., Wu, Y., & Wang, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25255-1_54
Ballesteros, M., Herrera, J., Francisco, V., & Gervás, P. (2010) · International Conference on Knowledge Discovery and Information Retrieval · doi:10.5220/0003116803360341
Building vietnamese dependency treebank based on Chinese-Vietnamese bilingual word alignment
Li, Y., Guo, J., Yu, Z., Wang, H., & Wen, Y. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603371
Experiments on Semi-supervised Dependency Parsing of a Morphologically Rich Language
Atutxa, A., Ezeiza, N., Goenaga, I., & Gojenola, K. (2015)
A Framework of Automatic Case Frame Construction From a Raw Corpus
Jin, G., Kawahara, D., & Kurohashi, S. (2012)
SyntaxNet Errors from the Linguistic Point of View
Durandin, O., Malafeev, A., & Zolotykh, N. Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-73013-4_4
ENTROPY-GUIDED FEATURE GENERATION FOR LARGE MARGIN STRUCTURED LEARNING
Fernandes, E. L. R., & Milidiú, R. L. (2018) · Monografias em Ciência da Computação · doi:10.17771/pucrio.dimcc.24327
Deep learning applications for transition-based dependency parsing
Elkaref, M. (2018) · University of Birmingham Institutional Research Archive (University of Birmingham)
Building Laos Dependency Treebank by Means of Chinese-Laos Bilingual Corpus of Word Alignment
Yin, R., Zhou, L., Zhou, F., & Li, F. (2017) · International Journal of Multimedia and Ubiquitous Engineering · doi:10.14257/ijmue.2017.12.2.03
Character Profiling in Low-Resource Language Documents
Wong, T., & Lee, J. S. Y. (2019) · Australasian Document Computing Symposium · doi:10.1145/3372124.3372129
Tanguy, L., Brunet, P., & Ferret, O. (2020) · HAL
Research on Annotation Rules and Recognition Algorithm Based on Phrase WindowPreprint
Liu, G., Tu, G., Li, Z., & Liu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03140
Research on multi-dimensional end-to-end phrase recognition algorithm based on background knowledgePreprint
Li, Z., Tu, G., Liu, G., Zhan, Z.-Q., & Liu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03860
Research on multi-dimensional end-to-end phrase recognition algorithm based on background knowledge
Yijian, L., Zheng, L., Gang, T., Guang, L., Zhiqiang, Z., & Xin, W. (2020) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice51518.2020.00010
Study on Phrase-Window-based Annotation Rules and an Identification Algorithm
Zhang, L., Jian, L., Wang, H., Tao, H., Wu, D., & Yijian, L. (2021) · International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) · doi:10.1109/icbaie52039.2021.9390020
NLP-Based Tools for Decoding the Language of Life
Chauhan, A., & Hasija, Y. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3097-2_18
Lê, N. L. (2020) · theses.fr (ABES) · doi:10.70675/4c31f893zf8eaz490bzb5fbz7cb2b7899c15
Knowledge based Annotation of Requirements and Reuse of Components
Fischer, W. (2007)
Metadata
- Authors
- McDonald, R., Lerman, K., & Pereira, F.
- Affiliations
- University of Pennsylvania (US)
- ORCID
- Pereira, Fernando
- Venue
- Proceedings of the Tenth Conference on Computational Natural Language Learning - CoNLL-X '06
- Published
- 2006
- Pages
- 216
- OpenAlex
- W2143541936
- Cited by
- 223
- References
- 22
- Citations by year
- 2022: 3 · 2021: 5 · 2020: 8 · 2019: 2 · 2018: 12 · 2017: 8 · 2016: 9 · 2015: 15 · 2014: 14 · 2013: 25 · 2012: 29
- FWCI
- 13.17
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Conference on Computational Natural Language Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- multilingual dependency parsing, morphological features, sequence classification, error analysis
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
