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.
1. The ways information extraction functions
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:
- It allows users to automate smart content classification, administration, integrated search, and distribution operations by extracting structured data from various texts.
- Conduct data-driven activities such as uncovering hidden links, mining for market trends and patterns, etc.
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:
- Finding and sorting different ideas or concepts: In this step, you must find and sort different ideas and concepts from unstructured data like people's mentions on social media, locations, events, things, and other pre-specified data sources.
- Ore-processing your text: At this stage, you must prepare the source text for further processing using computational linguistics tools, such as sentence splitting, tokenization, morphological analysis, and more.
- Unifying: For this subtask, you must present the data you have collected in a standard format to make it easy to read and use.
- Linking the concepts: For this subtask, you need to "join the dots" by assembling the ideas you have already sorted and figuring out how they are related.
- Enriching your database: This subtask involves putting the data or knowledge you've extracted into your new or existing databases so you can use it in the future.
- Getting rid of duplicate data: In this step, you need to get rid of all duplicate data so that you don't have to do the same tasks over and over again. This will save you time and effort.
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.
2. The examples of information extraction:
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:
- social media interactions
- medical records
- online video streams
- corporate reports
- Government documents, etc.
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
Event: MotoGP
Location: Valencia
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.
3. The uses of information extraction:
- Business intelligence: To help analysts extract structured information from different sources.
- Scientific research: To help researchers automate the discovery of relevant papers' suggestions and references.
- Financial investigation: To help financial professionals analyze, monitor, and discover hidden relationships between concepts and datasets.
- Media monitoring: It suits brands, individuals, and people’s mentions.
- Healthcare records management helps professionals extract, structure, and summarize patient records.
- Pharma research: It is suitable for pharmacists to discover drugs, their benefits, and adverse effects and analyze and automate clinical trials.
4. The different techniques of information extraction
The five standard data extraction techniques are discussed below.
a) Recognizing Named Entity:
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:
- Person: John, Ruth, Sofia, Nora Gray, Isabella Diaz, etc.
- Location: United States, California, Los Angeles, etc.
- Organization: J.P. Morgan, Walmart, Wells Fargo, etc.
- Date: February 1, 2023
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.
b) Analyzing Customer Sentiments:
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.
c) Aspect Mining:
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:
- Customer service - negative
- Agent - negative
- Call center - negative
- Pricing/Premium - positive
Such an output conveys the full intent of your source text.
d) Text Summarization:
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.
e) Topic Modeling:
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:
- Latent Semantic Analysis (LSA)
- Latent Dirichlet Allocation (LDA)
- Probabilistic Latent Semantic Analysis (PLSA)
- Correlated Topic Model (CTM).
Conclusion
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.
