Information extraction is finding entities in unstructured text sources, classifying them, and putting them in a database. Semantically enhanced information extraction, or "semantic annotation," combines these entities with semantic descriptions and links from a knowledge graph. By adding metadata to the extracted concepts, this solution solves many problems in enterprise content management and knowledge discovery.
Information extraction (IE) is getting information from multiple text sources that fit certain criteria. It is the process of automatically pulling out information from a text body about a certain topic. Users can use data extraction tools to get information from databases, text documents, websites, social media pages, and other places.
By extracting structured data from different texts, it allows users:
The process and functioning of information extraction involve a set of complex techniques. The data extraction technique transforms unstructured information from texts into groups or facts. In other words, data extraction pulls unstructured data and converts it into readable and usable reports. It includes formal texts, documents, readable statements, and other structured data.
To turn unstructured text bodies into structured information, you may have to do the following tasks:
The data extraction process can either be automated to save your vital resources or managed manually, and it is performed based on human inputs. However, we recommend a combination of both automation and human processing to maintain accuracy.
One common example of information extraction is when your email takes only the relevant information from the email body and adds it to your calendar, like when you have a meeting or event on a certain date. Other ways to get information from free-flowing text sources are to collect data from structured sources like -legal acts:
Here's a real-world example to help you better understand how information extraction works. Take a look at the following piece of news about Marc Marques and the Valencia MotoGP.
We can pull the facts from this free-flowing paragraph into a structured data format that machines can read.
Person: Marc Marquez
Related mentions: Maverick Vinales, Yamaha, Jorge Lorenzo
Let’s look at another example.
“Strokes are the third most common cause of death in America today.”
From the above sentence, we can extract the following datasets -
Most common/Top three causes of death in America today:
This is a simple instance of how we can pull facts and data from unstructured, free-flowing texts and how we can convert them into structured and usable information.
The five standard data extraction techniques are discussed below.
Named Entity Recognition (NER) is the basic NLP method to extract text entities. This includes the person’s name, location, demographics, dates, organizations, etc. It can highlight the key references and concepts present in the sample text. A NER output for a source text looks like this:
Named Entity Recognition (NER) is based on supervised models and grammar rules. But some NER platforms, like open NLP, already have NER models that have been trained.
In natural language processing, sentiment analysis is used a lot. Most of the time, it has to do with comments on social media, reviews of products or services, customer surveys, and any other place where buyers can give feedback and say what they think about a product or service. The most common way for sentiment analysis to show results is on a scale with three points: positive, negative, and neutral. But in some complicated situations, the output format may also include a number that shows how people feel about something.
As the name suggests, aspect mining helps find different parts of the original text. Part-of-speech tagging is one of the easiest aspects of mining techniques. Aspect mining and sentiment analysis can be used together to get complete information from the body of the text. When you use sentiment analysis with aspect mining, you can get the following results:
Such an output conveys the full intent of your source text.
Text summarization summarizes large chunks of text in research papers and news articles. Extraction and abstraction are the two main ways to summarize a text. The first one helps summarize the text by pulling out smaller parts. On the other hand, the second method lets users make summaries by collecting new text that captures the main points of the source text.
This complicated process helps marketers or data scientists find natural topics in a text source. Topic modeling is an unsupervised method, so it doesn't need training datasets with labels or model training. Some of the most important algorithms for topic modeling are:
After using the above methods to get the data you need from an unstructured text source, you can turn it into information you can use and understand. This structured data can be saved for later use. Later, you can use it directly or group machine learning models and activities together to make them work better and more accurately.