The DOI matches its Crossref record.
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
Open accessAbstract
Welcome to EMNLP 2010, Conference on Empirical Methods in Natural Language Processing! The conference is organized by SIGDAT, the Association for Computational Linguistics' special interest group on linguistic data and corpus-based approaches to NLP, and is held this year as a stand-alone conference at the MIT Stata Center, Massachusetts, USA on October 9-11. EMNLP 2010 received 500 submissions, a new record for the conference. The program committee was able to accept 125 papers in total (an acceptance rate of 25%). Among them, 70 of the papers (14%) were accepted for oral presentations, and 55 (11%) for poster presentations. The PC, which consists of 18 area chairs and 460 PC members from Asia, Europe, and North America, worked together to create a strong program with high quality oral and poster presentations and enlightening invited talks.
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
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
Deep Learning--based Text Classification: A Comprehensive Review
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2022) · ACM Computing Surveys · doi:10.1145/3439726 · Cited by 1,538
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948
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
A survey on sentiment analysis challenges
Hussein, D. M. E.-D. M. (2018) · Journal of King Saud University - Engineering Sciences · doi:10.1016/j.jksues.2016.04.002 · Cited by 659
Metadata
- Published
- 2013
- OpenAlex
- W1516184288
- Cited by
- 1,265
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. https://app.cochat.ai/papers/10.18653/v1/d13-1
- The paper
- Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. (2013). https://doi.org/10.18653/v1/d13-1
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.
