Whilst there have been recent attempts to crack the problem without it, the crux of the issue is really that for approach to learn it needs a large corpus of marked up training data (there are some marked up corpora available, but the problem is still quite domain specific, so training on the WSJ data might not perform particularly well against your domain specific data) and finding a set of 100,000 marked up sentences is no easy feat. There are some approaches that can be used to tackle this by generating training data - but it can be hard to generate truly representative data and so this approach always risks over-fitting to the generated data.
Having previously looked at Stanford's NLP library for some sentiment analysis, this time I am looking at using the OpenNLP library. Stanford's library is often referred to as the benchmark for several NLP problems, however, these benchmarks are always against the data it is trained for - so out of the box, we likely won't get amazing results against a custom dataset. Further to this, the Stanford library is licensed under GPL which makes it harder to use in any kind of commercial/startup setting. The OpenNLP library has been around for several years, but one of its strengths is its API - its pretty well documented to get up and running, and is all very extendable.
Training a custom NEROnce again, for this exercise we are going back to the BBC recipe archive for the source data - we are going to try and train an OpenNLP model that can identify ingredients.
To train the model we need some example sentences - they recommend at least 15,000 marked up sentences to train a model - so for this I annotated a bunch of the recipe steps and ended up with somewhere in the region of about 45,000 sentences.
As you can see in the above example, the marked up sentences are quite straight forward. We just wrap the ingredient in the tags as above (although note that if the word itself isn't padded by a space either side inside the tags, it will fail!).
Once we have our training data, we can just easily setup some code to feed it in and train our model:
This is a very simple example of how you can do it, and not always paying attention to engineering best practices, but you get the idea for whats going on. We are getting an input stream of our training data set, then we instantiate the Maximum Entropy name finder class and ask it to train a model, which we can then write to disk for future use.
When we want to use the model, we can simply load it back into the OpenNLP Name Finder class and use that to parse the input text we want to check:
So, once I had created some training data in the required format, and trained a model I wanted to see how well it had actually worked - obviously, I don't want to run it against one of the original recipes as they were used to train the model, so I selected this recipe for rosemary-caramel millionaire shortbread, to see how it performed, here are the ingredients it found:
All in all, pretty good - it missed some ingredients, but given the training data was created in about 20 minutes just manipulating the original recipe set with some groovy, that's to be expected really, but it did well in not returning false positives.
In conclusion, if you have a decent training set, or have the means to generate some data with a decent range, you can get some pretty good results using the library. As usual, the code for the project is on GitHub (although it is little more than the code shown in this post).