For much of the first year or two in the life of Web services - and indeed all of their history up to that point - they were about remote procedure calls (RPC); exposing remote APIs across the Internet in order to facilitate machine-to-machine communication and ultimately, business-to-business integration over the Internet. It didn’t take very long however, for Web services proponents to realize that they needed to distance themselves from RPC and its well-deserved reputation as a poor large scale integration architectural style, due to the failure of systems such as CORBA, DCOM, and RMI to see any widespread use on the Internet. So, sometime in 2000/2001, collective wisdom in the space shifted towards a preference for “document oriented” services. Vendors quickly jumped on board with upgraded toolkits, and that was that; documents were the New Big Thing.
Flesh is a cross-platform, open source Java application designed to quickly analyze a document and display the difficulty associated with comprehending it. It is available for all platforms that support Java. Flesh has been released under the GPL (license for use).
After processing a document, Flesh produces two scores: the Flesch-Kincaid Grade Level and the Flesch Reading Ease Score. Each of these scores is calculated after determining the number of sentences, words and syllables a document contains. Using those numbers, the Flesch-Kincaid Grade Level and Flesch Reading Ease Score can then be calculated
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page 1480--1489. San Diego, California, Association for Computational Linguistics, (June 2016)
Q. Le, and T. Mikolov. Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, page 1188--1196. Bejing, China, PMLR, (June 2014)
P. Kathiria, and S. Ahluwalia. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 5 (1):
53 - 62(February 2016)
V. Franzoni, Y. Li, C. Leung, and A. Milani. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7974 LNCS (PART 4):
657-672(January 2017)
S. Wang, J. Tang, C. Aggarwal, and H. Liu. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM, (October 2016)
N. Weibel, A. Ispas, B. Signer, and M. Norrie. CHI '08 Extended Abstracts on Human Factors in Computing Systems, page 2349--2354. New York, NY, USA, ACM, (2008)Demo.
D. Kim, C. Park, J. Oh, S. Lee, and H. Yu. Proceedings of the 10th ACM Conference on Recommender Systems, page 233--240. New York, NY, USA, ACM, (2016)
K. Bache, D. Newman, and P. Smyth. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, page 23--31. ACM, (2013)
N. Slonim, and N. Tishby. SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, page 208--215. New York, NY, USA, ACM, (2000)
S. Nunes, C. Ribeiro, and G. David. Proceedings of the 9th Annual ACM International Workshop on Web Information and Data Management, page 129--136. New York, NY, USA, ACM, (2007)
E. Boese, and A. Howe. Proceedings of the 14th ACM International Conference on Information and Knowledge Management, page 632--639. New York, NY, USA, ACM, (2005)
A. Jatowt, C. Au Yeung, and K. Tanaka. Proceedings of the 22Nd ACM International Conference on Conference on Information &\#38; Knowledge Management, page 2273--2278. New York, NY, USA, ACM, (2013)
M. Schuhmacher, and S. Ponzetto. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, page 543--552. New York, NY, USA, ACM, (2014)
T. Elsayed, J. Lin, and D. Oard. Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, page 265--268. Stroudsburg, PA, USA, Association for Computational Linguistics, (2008)
K. Chandrasekaran, S. Gauch, P. Lakkaraju, and H. Luong. Adaptive Hypermedia and Adaptive Web-Based Systems, volume 5149 of Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, (2008)