Ml-knn: A lazy learning approach to multi-label learning (2007)(About) First identify K nearest neighbors in the training set, then, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance
Natural Language Processing (almost) from Scratch - Collobert and Weston (2011)(About) seminal work
> a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements
Convolutional Neural Networks for Sentence Classification (2014)(About) experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
[Github project](https://github.com/yoonkim/CNN_sentence) with code, updates to paper, and links to valuable resources, such as a [Denny Britz](/tag/denny_britz)'s [implementation in TensorFlow](https://github.com/dennybritz/cnn-text-classification-tf)