Search Results for "paper"

The text structures of notes and publications in music theory and musical analysis bring challenging requirements: how to include music notation excerpts, graphics, and even combinations thereof, into the typeset flow of paragraphs and into the work-flow, and how to integrate navigable references to these and to single domain entities into running text. Furthermore, dynamic interactive documents can be useful for presenting complicated interdependencies to the reader more clearly, far beyond conventional paper publication. The mulitNotes text architecture and processing pipeline is based on d2d and standard technologies (XSLT, ECMAScript. LilyPond, PostScript, etc.) and addresses these issues. An overview about the software architecture and its operation is given in: Journal of the Text Encoding Initiative, Open Issue 18/2024: "Using d2d for Writing XML --- The multiNotes Text Architecture for Musical Analysis" https://doi.org/10.4000/132ex

...DCTFinder combines heuristic title detection, supervised learning with Conditional Random Fields (CRFs) for document date extraction, and rule-based creation time recognition. DCTFinder is released under CeCILL free software license agreement. The system is described in the following paper (see 'Files' section): Xavier Tannier. "Extracting News Web Page Creation Time with DCTFinder". Proceedings of the 9th Language Resources and Evaluation Conference. Reykjavik, Iceland.

...README: https://sourceforge.net/projects/biolemmatizer/files/ The BioLemmatizer 1.2 release adds an optional functionality to normalize British English spellings into American English spellings and then retrieve corresponding lemmas. If you use the BioLemmatizer to support academic research, please cite the following paper: Haibin Liu, Tom Christiansen, William A Baumgartner Jr, and Karin Verspoor BioLemmatizer: a lemmatization tool for morphological processing of biomedical text Journal of Biomedical Semantics 2012, 3:3.

...Event extraction includes finding events and the parameters for an event in a text. The method is based on SVM but other ML algorithms can be adopted. The method details are explained in the following paper: Ehsan Emadzadeh, Azadeh Nikfarjam, and Graciela Gonzalez. 2011. Double Layered Learning for Biological Event Extraction from Text. In Proceedings of the BioNLP 2011 Workshop Companion Volume for Shared Task, Portland, Oregon, June. Association for Computational Linguistic

Java Suffix array library for phrase discovery. Inspired initially by the classic paper of Yamamoto & Church, with newer ideas from Abouelhoda et al and Kim et al. Adapted for large alphabet so that words can be tokenized as alphabet characters.