A Note on the Non-Existence of Functors

We consider several cases of non-existence theorems for functors. For example, there are no nontrivial functors from the category of sets, (or the category of groups, or vector spaces) to any small category. See 2.3. Another kind of nonexistence is that of (co-)augmented functors. For example, every augmented functor from groups to abelian groups, is trivial, i.e. has a trivial augmentation map. Every surjective co-augmented functor from groups to perfect groups or to free groups is also trivial

Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules

Abstract Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word- and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, we’ve developed a Python package with a minimalistic user interface