Strategy for parsing natural language descriptions into structured data

Question

I have a set of requirements and I'm looking for the best Java-based strategy / algorthm / software to use. Basically, I want to take a set of recipe ingredients entered by real people in natural english and parse out the meta-data into a structured format (see requirements below to see what I'm trying to do).

I've looked around here and other places, but have found nothing that gives a high-level advice on what direction follow. So, I'll put it to the smart people :-):

What's the best / simplest way to solve this problem? Should I use a natural language parser, dsl, lucene/solr, or some other tool/technology? NLP seems like it may work, but it looks really complex. I'd rather not spend a whole lot of time doing a deep dive just to find out it can't do what I'm looking for or that there is a simpler solution.

Requirements

Given these recipe ingredient descriptions....

1. "8 cups of mixed greens (about 5 ounces)"
2. "Eight skinless chicken thighs (about 1¼ lbs)"
3. "6.5 tablespoons extra-virgin olive oil"
4. "approximately 6 oz. thinly sliced smoked salmon, cut into strips"
5. "2 whole chickens (3 .5 pounds each)"
6. "20 oz each frozen chopped spinach, thawed"
7. ".5 cup parmesan cheese, grated"
8. "about .5 cup pecans, toasted and finely ground"
9. ".5 cup Dixie Diner Bread Crumb Mix, plain"
10. "8 garlic cloves, minced (4 tsp)"
11. "8 green onions, cut into 2 pieces"

I want to turn it into this....

|-----|---------|-------------|-------------------------|--------|-----------|--------------------------------|-------------|
|     | Measure |             |                         | weight | weight    |                                |             |
| #   | value   | Measure     | ingredient              | value  | measure   | preparation                    | Brand Name  |
|-----|---------|-------------|-------------------------|--------|-----------|--------------------------------|-------------|
| 1.  | 8       | cups        | mixed greens            | 5      | ounces    | -                              | -           |
| 2.  | 8       | -           | skinless chicken thigh  | 1.5    | pounds    | -                              | -           |
| 3.  | 6.5     | tablespoons | extra-virgin olive oil  | -      | -         | -                              | -           |
| 4.  | 6       | ounces      | smoked salmon           | -      | -         | thinly sliced, cut into strips | -           |
| 5.  | 2       | -           | whole chicken           | 3.5    | pounds    | -                              | -           |
| 6.  | 20      | ounces      | forzen chopped spinach  | -      |           | thawed                         | -           |
| 7.  | .5      | cup         | parmesean cheese        | -      | -         | grated                         | -           |
| 8.  | .5      | cup         | pecans                  | -      | -         | toasted, finely ground         | -           |
| 9.  | .5      | cup         | Bread Crumb Mix, plain  | -      | -         | -                              | Dixie Diner |
| 10. | 8       | -           | garlic clove            | 4      | teaspoons | minced                         | -           |
| 11. | 8       | -           | green onions            | -      | -         | cut into 2 pieces              | -           |
|-----|---------|-------------|-------------------------|--------|-----------|--------------------------------|-------------|

Note the diversity of the descriptions. Some things are abbreviated, some are not. Some numbers are numbers, some are spelled out.

I would love something that does a perfect parse/translation. But, would settle for something that does reasonably well to start.

Bonus question: after suggesting a strategy / tool, how would you go about it?

Thanks!

Joe

Answer 1

Short answer. Use GATE.

Long answer. You need some tool for pattern recognition in text. Something, that can catch patterns like:

{Number}{Space}{Ingredient}
{Number}{Space}{Measure}{Space}{"of"}{Space}{Ingredient}
{Number}{Space}{Measure}{Space}{"of"}{Space}{Ingredient}{"("}{Value}{")"}
...

Where {Number} is a number, {Ingredient} is taken from dictionary of ingredients, {Measure} - from dictionary measures and so on.

Patterns I described are very similar to GATE's JAPE rules. With them you catch text that matches pattern and assign some label to each part of a pattern (number, ingredient, measure, etc.). Then you extract labeled text and put it into single table.

Dictionaries I mentioned can be represented by Gazetteers in GATE.

So, GATE covers all your needs. It's not the easiest way to start, since you will have to learn at least GATE's basics, JAPE rules and Gazetteers, but with such approach you will be able to get really good results.