We present an approach to using multiple preprocessing schemes to improve statistical word alignments. We show a relative reduction of alignment error rate of about 38%.
We describe a word alignment platform which ensures text pre-processing (tokenization, POS-tagging, lemmatization, chunking, sentence alignment) as required by an accurate word al...
Dan Tufis, Radu Ion, Alexandru Ceausu, Dan Stefane...
Abstract-In this paper, we improve the idea of the near-optimal alignments. Though the near optimal alignments increase the possibility to find the correct alignment, too many of t...
For many performance analysis problems, the ability to reason across traces is invaluable. However, due to non-determinism in the OS and virtual machines, even two identical runs ...
We describe a novel hardware architecture for genomic and proteomic sequence alignment which achieves a speed-up of two to three orders of magnitude over Smith-Waterman dynamic pr...