Working with a large amount of text data definitely exhausts you and takes a lot of your time. That’s why many companies prefer Information Extraction techniques to reduce human error and improve efficiency.
In this article, we’ll aim at building information extraction algorithms on unstructured data using text extraction, Deep Learning, and Natural Language Processing (NLP) techniques.
Table of contents:
Information extraction refers to the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most cases, this activity concerns processing human language texts by means of natural language.
It’s possible for us to manually search for the required information from a few documents. However, we can easily and automatically extract this data with the help of information extraction NLP algorithms.
There are many ways you can apply to pull out information and the most common one comes to Named Entity Recognition. It depends on your business niche and market that you own different types of data, from recipes to resumes and medical reports or invoices. So this method can ensure the deep learning model is specific to a suitable use case.
As mentioned, you should be clear about the kind of data you are working on. For example, for medical reports, you should define extracting patient names, drug information, sick information, etc. In terms of recruitment, it’s necessary to extract data based on Name, Contact Info, Skills, Education, and Working Experience attributes.
After that, we’ll start applying the information extraction to process and build a deep learning model around the data. We’ll show you how to do it with NER spacy as follows.
Spacy is a free and open-source advanced Natural Language Processing (NLP) in python.
It’s designed specifically for production use and helps you build applications that process and “understand” large volumes of text. You can make the most of spacy to build Information Extraction or natural language understanding systems or to pre-process text for deep learning.
Here is an example of how to use spacy to extract information:
First, use a terminal or command prompt and type in the following command to download the spacy pre-trained model after installing the latest version of spacy.
python -m spacy download en_core_web_trf
Code:
#import spacy library
import spacy
from spacy import displacy
#load pre-trained spacy model
nlp = spacy.load("en_core_web_trf")
#load data
doc = nlp("NASA awarded Elon Musk’s SpaceX a $2.9 billion contract to build the lunar lander.")
#predict entities in sentence above
for ent in doc.ents:
print(ent.text, ent.label_)
displacy.render(doc, style="ent", jupyter =True)
Output:
It works!! Let’s dive deep into how spacy performs it.
In the example above, we’ve imported the spacy module into the program. Then, we load the pre-trained spacy model and after that, we load data into the model and store it in a doc variable. Now we iterate over the doc variable to find the entities that the pre-trained model has been learned.
A standard resume contains various information related to the Experience, Education Background, Skills, and Personal Information of a candidate. The information can be presented in multiple ways, or not present at all. So, making an intelligent resume parser tool to look for information became a huge challenge.
The reason we mentioned above proves that statistical methods like Naïve Bayes failed here. Therefore, the NER algorithm rescues and allows everyone on the team to search and analyze important details across business processes.
You must stay careful in some steps while creating a deep learning model for the Resume parser:
First, the dataset preparation was the most important process. Anyone who wants to build their deep learning model should start thinking about this part from the very early stage. We then prepare unlabeled training data and search for tools to help us perform the manual annotation.
Next, choosing a suitable model mostly depends on the types of data you’re working with. The spacy library does support many state-of-the-art models that we could use. However, utilizing the pre-trained models and fine-tuning them based on our data should be a challenge for researchers. They will need to experiment on the hyperparameters and fine-tune the model correctly.
Plus, tracking the model with the right evaluation metrics enables you to find out which models are suitable for your business. In our resumes parser system, by tracking the model performance using the F-1 score, the model had crossed our benchmark of 85%.
We’ve walked you through the basic knowledge about information extraction techniques from text data. And then, we’ve seen how important NER is, especially when working with many documents.
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