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Performance Time Report
A performance time evaluation between a C lexer and a Lex generated lexer.
Carmina Pérez Guerrero
Gabrielli Laurindo's CV
Gabrielli Laurindo Curriculum
Created with the Medium Length Professional CV template
Shaila Ang's CV
Shaila Ang's CV
Created with the CV for Freshers template.
Survey on Bi-LSTM CNNs CRF for Italian Sequence Labeling and Multi-Task Learning
In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.
PHYS 24L Final Assignment
A final assignment for measurement and error propagation in a sophomore physics lab course.
Deborah K Fygenson