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Effect of Non-linear Deep Architecture in Sequence Labeling
"If we compare the widely used Conditional Random Fields (CRF) with newly proposed “deep architecture” sequence models (Collobert et al., 2011), there are two things changing: from linear architecture to non-linear, and from discrete feature representation to distributional. It is unclear, however, what utility non-linearity offers in conventional feature-based models. In this study, we show the close connection between CRF and “se-
quence model” neural nets, and present an empirical investigation to compare their performance on two sequence labeling tasks – Named Entity Recognition and Syntactic Chunking. Our results suggest that non-linear models are highly effective in low-dimensional distributional spaces. Somewhat surprisingly, we find that a non-linear architecture offers no benefits in a high-dimensional discrete feature space."
nlp  ner  deep-learning  crf 
october 2018 by arsyed
ai  ml  HMM  CRF 
september 2018 by dlutcat
jiesutd/NCRFpp: NCRF++, an Open-source Neural Sequence Labeling Toolkit. It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components. (ACL 2018 demo paper)
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
crf  pytorch  deep-learning  github  code 
june 2018 by nharbour

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