The DOI matches its OpenAlex record.
Generating Typed Dependency Parses from Phrase Structure Parses
Open accessAbstract
This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses.In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used.We provide a comparison of our system with Minipar and the Link parser.The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.
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.
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
Cited by
An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879
“thod works at a finer level and embeds fragments of images and fragments of sentences. This method breaks down the images into a number of objects and sentences into a dependency tree relations (DTR) [28] and reasons about their latent, inter-modal alignment. It shows that the method achieves significant improvements…”
MethodNRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
Kiritchenko, S., Zhu, X., Cherry, C., & Mohammad, S. (2014) · Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) · doi:10.3115/v1/s14-2076 · Cited by 697
(2019) · doi:10.18653/v1/n19-1419 · Cited by 601
(2011) · Cited by 446
Designing and Interpreting Probes with Control Tasks
Hewitt, J., & Liang, P. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1275 · Cited by 393
Stop clickbait: detecting and preventing clickbaits in online news media
Chakraborty, A., Paranjape, B., Kakarla, S., & Ganguly, N. (2016) · 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752207 · Cited by 368
(2012) · MPG.PuRe (Max Planck Society) · Cited by 360
Searching by Talking: Analysis of Voice Queries on Mobile Web Search
Guy, I. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911525 · Cited by 140
Overcoming the Lack of Parallel Data in Sentence Compression
Filippova, K., & Altun, Y. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1155 · Cited by 132
Toward human-centered algorithm design
Baumer, E. P. (2017) · Big Data & Society · doi:10.1177/2053951717718854 · Cited by 125
Literature Based Discovery: Models, methods, and trends
Henry, S., & McInnes, B. T. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.08.011 · Cited by 121
Designing and Interpreting Probes with Control Tasks
Hewitt, J., & Liang, P. (2019) · arXiv · doi:10.48550/arxiv.1909.03368 · Cited by 110
That is your evidence?: Classifying stance in online political debate
Walker, M. A., Anand, P., Abbott, R., Tree, J. E. F., Martell, C., & King, J. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.05.032 · Cited by 104
Recovering from failure by asking for help
Knepper, R. A., Tellex, S., Li, A., Roy, N., & Rus, D. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9460-1 · Cited by 76
The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries
Guy, I. (2018) · ACM Transactions on Information Systems · doi:10.1145/3182163 · Cited by 71
Dependency parsing of learner English
Huang, Y., Murakami, A., Alexopoulou, T., & Korhonen, A. (2018) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.16080.hua · Cited by 68
Measuring machine translation quality as semantic equivalence: A metric based on entailment features
Padó, S., Cer, D., Galley, M., Jurafsky, D., & Manning, C. D. (2009) · Machine Translation · doi:10.1007/s10590-009-9060-y · Cited by 47
Self-citationRoberts, K., Rink, B., & Harabagiu, S. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001619 · Cited by 45
Perikos, I., Grivokostopoulou, F., & Hatzilygeroudis, I. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0139-y · Cited by 44
Surveyor: A System for Generating Coherent Survey Articles for Scientific Topics
Jha, R., Coke, R., & Radev, D. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9495 · Cited by 19
Dependency Parsing for Extracting Family History
Lewis, N., Gruhl, D., & Yang, H. (2011) · 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology · doi:10.1109/hisb.2011.23 · Cited by 7
Metadata
- Authors
- de Marneffe, M.-C., MacCartney, B., & Manning, C. D.
- Venue
- Language Resources and Evaluation Conference (LREC)
- Published
- 2006-05-01
- Pages
- 449-454
- OpenAlex
- W1508977358
- Cited by
- 2,258
- References
- 0
- FWCI
- 40.58
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.