How Many Pages?
How Many Pages?
Being able to predict the length of a scientific paper may be helpful in numerous situations. This work defines the paper length prediction task as a regression problem and reports several experimental results using popular machine learning models. We also create a huge dataset of publication metadata and the respective lengths in number of pages. The dataset will be freely available and is intended to foster research in this domain. As future work, we would like to explore more advanced regressors based on neural networks and big pretrained language models.
Comment: 5 pages, 6 tables. Published in proceedings of NLPIR 2020, the 4th International Conference on Natural Language Processing and Information Retrieval, Seoul, Korea
- Charles University Czech Republic
Microsoft Academic Graph classification: Computer science computer.software_genre Task (project management) Domain (software engineering) Regression problems Information retrieval Artificial neural network business.industry Metadata Language model Artificial intelligence business computer Natural language processing
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Information Retrieval, Machine Learning (cs.LG), Computer Science - Computation and Language, Computation and Language (cs.CL), Information Retrieval (cs.IR)
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Information Retrieval, Machine Learning (cs.LG), Computer Science - Computation and Language, Computation and Language (cs.CL), Information Retrieval (cs.IR)
Microsoft Academic Graph classification: Computer science computer.software_genre Task (project management) Domain (software engineering) Regression problems Information retrieval Artificial neural network business.industry Metadata Language model Artificial intelligence business computer Natural language processing
[2] Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (01 Oct 2001), 5-32.
[3] Erion Çano and Ondřej Bojar. 2019. Effic iency Metrics for Data-Driven Mode ls: A Text Summarization Case Study. In Proceedings of the 12th International Conference on Natural Language Generation. Association for Computational Linguistics, Tokyo, Japan, 229-239.
[30] Jie Tang, Jing Zhang, Limin Yao, Jua nzi Li, Li Zhang, and Zhong Su. 2008. ArnetMiner: Extraction and Mining of Academic Soc ia l Networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Las Vegas, Nevada, USA) (KDD '08). ACM, New York, NY, USA, 990-998.
[31] Tin Kam Ho. 1995. Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1. Montrea l, Canada, 278-282.
[32] A Wendemuth. 1995. Learning the unlearnable. Journal of Physics A: Mathematical and General 28, 18 (sep 1995), 5423-5436. [OpenAIRE]
[33] B. Widrow and M. A. Lehr. 1990. 30 years of adaptive neural networks: perceptron, Mada line, and backpropagation. Proc. IEEE 78, 9 (1990), 1415-1442.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!