Extracting Structured Data from Recipes Using Conditional Random Fields (2015)
This is the kind of problem for which LSTM RNNs -- and more recently, fully-attention-based deep neural nets -- produce state-of-the-art results.I wonder if the author ever tried using, say, an AWD LSTM RNN[a] or a Transformer-like model[b] for this task.Using an RNN or an attention model for this would eliminate the need for feature engineering such as:  feature_1 = 1 if x_t is capitalized and y_t equals "NAME";
              0 otherwise.

This is one of seven carefully engineered feature functions listed in the article, and the author states that the seven are only a partial list.Moreover, using a modern RNN or attention model likely would produce better predictions, with much better generalization.[a] https://arxiv.org/abs/1708.02182 / https://github.com/salesforce/awd-lstm-lm[b] https://arxiv.org/abs/1706.03762 / https://github.com/tensorflow/tensor2tensor