Techniques other than RegEx to discover 'intent' in sentences

Question

I'm embarking on a project for a non-profit organization to help process and classify 1000's of reports annually from their field workers / contractors the world over. I'm relatively new to NLP and as such wanted to seek the group's guidance on the approach to solve our problem.

I'll highlight the current process, and our challenges and would love your help on the best way to solve our problem.

Current process: Field officers submit reports from locally run projects in the form of best practices. These reports are then processed by a full-time team of curators who (i) ensure they adhere to a best-practice template and (ii) edit the documents to improve language/style/grammar.

Challenge: As the number of field workers increased the volume of reports being generated has grown and our editors are now becoming the bottle-neck.

Solution: We would like to automate the 1st step of our process i.e., checking the document for compliance to the organizational best practice template

Basically, we need to ensure every report has 3 components namely: 1. States its purpose: What topic / problem does this best practice address? 2. Identifies Audience: Who is this for? 3. Highlights Relevance: What can the reader do after reading it?

Here's an example of a good report submission.

"This document introduces techniques for successfully applying best practices across developing countries. This study is intended to help low-income farmers identify a set of best practices for pricing agricultural products in places where there is no price transparency. By implementing these processes, farmers will be able to get better prices for their produce and raise their household incomes."

As of now, our approach has been to use RegEx and check for keywords. i.e., to check for compliance we use the following logic: 1 To check "states purpose" = we do a regex to match 'purpose', 'intent' 2 To check "identifies audience" = we do a regex to match with 'identifies', 'is for' 3 To check "highlights relevance" = we do a regex to match with 'able to', 'allows', 'enables'

The current approach of RegEx seems very primitive and limited so I wanted to ask the community if there is a better way to solving this problem using something like NLTK, CoreNLP.

Thanks in advance.

Answer 1

I've been trying to do something very similar with clinical trials, where most of the data is again written in natural language.

If you do not care about past data, and have control over what the field officers write, maybe you can have them provide these 3 extra fields in their reports, and you would be done.

Otherwise; CoreNLP and OpenNLP, the libraries that I'm most familiar with, have some tools that can help you with part of the task. For example; if your Regex pattern matches a word that starts with the prefix "inten", the actual word could be "intention", "intended", "intent", "intentionally" etc., and you wouldn't necessarily know if the word is a verb, a noun, an adjective or an adverb. POS taggers and the parsers in these libraries would be able to tell you the type (POS) of the word and maybe you only care about the verbs that start with "inten", or more strictly, the verbs spoken by the 3rd person singular.

CoreNLP has another tool called OpenIE, which attempts to extract relations in a sentence. For example, given the following sentence

Born in a small town, she took the midnight train going anywhere

CoreNLP can extract the triple

she, took, midnight train

Combined with the POS tagger for example; you would also know that "she" is a personal pronoun and "took" is a past tense verb.

These libraries can accomplish many other tasks such as tokenization, sentence splitting, and named entity recognition and it would be up to you to combine all of these tools with your domain knowledge and creativity to come up with a solution that works for your case.

Answer 2

If you don't provide yourself hints about the format of the document this is an open problem.

What you can do thought, is ask people writing report to conform to some format for the document like having 3 parts each of which have a pre-defined title like so

1. Purpose

Explains the purpose of the document in several paragraph.

2. Topic / Problem

This address the foobar problem also known as lorem ipsum feeling text.

3. Take away

What can the reader do after reading it?

You parse this document from .doc format for instance and extract the three parts. Then you can go through spell checking, grammar and text complexity algorithm. And finally you can extract for instance Named Entities (cf. Named Entity Recognition) and low TF-IDF words.

Answer 3

This seems like a perfect scenario to apply some machine learning to your process.

First of all, the data annotation problem is covered. This is usually the most annoying problem. Thankfully, you can rely on the curators. The curators can mark the specific sentences that specify: audience, relevance, purpose.

Train some models to identify these types of clauses. If all the classifiers fire for a certain document, it means that the document is properly formatted.

If errors are encountered, make sure to retrain the models with the specific examples.

Answer 4

Interesting problem, i believe its a thorough research problem! In natural language processing, there are few techniques that learn and extract template from text and then can use them as gold annotation to identify whether a document follows the template structure. Researchers used this kind of system for automatic question answering (extract templates from question and then answer them). But in your case its more difficult as you need to learn the structure from a report. In the light of Natural Language Processing, this is more hard to address your problem (no simple NLP task matches with your problem definition) and you may not need any fancy model (complex) to resolve your problem.

You can start by simple document matching and computing a similarity score. If you have large collection of positive examples (well formatted and specified reports), you can construct a dictionary based on tf-idf weights. Then you can check the presence of the dictionary tokens. You can also think of this problem as a binary classification problem. There are good machine learning classifiers such as svm, logistic regression which works good for text data. You can use python and scikit-learn to build programs quickly and they are pretty easy to use. For text pre-processing, you can use NLTK.

Since the reports will be generated by field workers and there are few questions that will be answered by the reports (you mentioned about 3 specific components), i guess simple keyword matching techniques will be a good start for your research. You can gradually move to different directions based on your observations.