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.<p>I wonder if the author ever tried using, say, an AWD LSTM RNN[a] or a Transformer-like model[b] for this task.<p>Using an RNN or an attention model for this would eliminate the need for feature engineering such as:<p><pre><code>  feature_1 = 1 if x_t is capitalized and y_t equals "NAME";
              0 otherwise.
</code></pre>
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.<p>Moreover, using a modern RNN or attention model likely would produce better predictions, with much better generalization.<p>[a] <a href="https://arxiv.org/abs/1708.02182" rel="nofollow">https://arxiv.org/abs/1708.02182</a> / <a href="https://github.com/salesforce/awd-lstm-lm" rel="nofollow">https://github.com/salesforce/awd-lstm-lm</a><p>[b] <a href="https://arxiv.org/abs/1706.03762" rel="nofollow">https://arxiv.org/abs/1706.03762</a> / <a href="https://github.com/tensorflow/tensor2tensor" rel="nofollow">https://github.com/tensorflow/tensor2tensor</a>