Natural Language Processing: What Is It and What Is Its Role in AI Data Mining?
Data is the new oil in today’s digital age. No matter what industry you operate in, data will be a valuable asset to guide your decision-making—provided that you use the correct methods to extract value from it. So, where oil fuels gas and chemicals, data is the fuel for analytics and artificial intelligence (AI).
AI, in particular, has enabled organisations to automate processes and scale quickly while maintaining a lean cost structure. However, to achieve these benefits, your business needs to be data-driven—and the common challenge here is dealing with massive volumes of unstructured data. Fortunately, this is where natural language processing comes into the picture.
Get to know more about natural language processing and its role in AI data mining below.
What Is Natural Language Processing (NLP)?
Natural language processing (NLP) is a branch of AI that enables machines to process and understand human language. It involves analysing different aspects of natural language, such as syntax and semantics, and then transforming this knowledge into machine learning algorithms to perform repetitive tasks.
Working with natural language can be challenging and complex since it is highly unstructured. For one, people have different styles and tones when speaking or writing—and to add to this, language changes based on geographic and social factors and is constantly evolving. For instance, some may use colloquialisms that do not always have formal definitions.
Overall, the ambiguity and variability of human language make it difficult for computers to understand natural languages. NLP thus tries to narrow that gap by making computers more “intelligent.” One familiar example of an NLP application would be virtual assistants such Siri and Amazon Alexa, which use NLP to understand and reply to user queries.
The Role of NLP in AI Data Mining
Now that you are more familiar with NLP basics, you might be wondering what role it plays in AI data mining. Given that NLP focuses on linguistics, it is primarily used in text mining, a sub-field of data mining that involves converting unstructured text into structured data for analysis.
Text mining and NLP go hand-in-hand to help you understand large volumes of textual content and uncover valuable insights. Essentially, text mining uses different methodologies to process text, one of which is NLP. Therefore, it plays a crucial role in the data preparation stage as it helps text mining applications understand the data you feed into it.
Essentially, text mining is only concerned with understanding the structure of textual data by discovering patterns and hidden information. However, given the nuances of linguistics, you need NLP to understand the meaning of the textual data. Otherwise, you may not get valuable insights from the mining process.
To better understand this, here are the common NLP techniques used to help machines read and understand text:
- Summarisation: As the term suggests, summarisation involves creating a concise summary from a long slab of text to highlight key or main points. This technique is often used in journalism to summarise news articles and shorten the time needed to comprehend the content.
- Part-of-Speech (POS) Tagging: POS tagging converts sentences into forms by assigning a corresponding tag to each word based on its part of speech. This process can be complicated since words may have different POS tags based on how they are used in a sentence, so you cannot map out the tags manually and need to use a machine.
- Text Classification: Text classification involves categorising unstructured text data into pre-defined groups or topics based on their content. Some famous use cases for this process include spam detection in emails and topic labeling for research papers.
- Sentiment Analysis: This technique detects the sentiment conveyed in text to classify it as positive, neutral, or negative sentiment. It is commonly used to analyse customer feedback and reviews to understand specific brands, products, or service perceptions.
- Topic Modeling: Topic modeling uses unsupervised learning to determine the topic or set of issues in a given text by scanning through the word and phrase patterns and grouping similar clusters together.
- Stemming and Lemmatisation: Stemming and lemmatisation are used to extract the root forms of derived words to understand their meaning better. Stemming involves finding the word’s stem, while lemmatisation analyses the word’s context to find the root word.
Value of NLP for Businesses
As mentioned at the start of this blog, data is the new oil. However, as valuable as this resource may be, it is only helpful if you know how to analyse it and find actionable insights. The reality though, is that 80-90% of data is unstructured, so you need to use proper techniques to analyse it.
NLP is one technique that helps you parse through large volumes of textual data and understand its meaning to extract relevant insights. With NLP, you can achieve benefits such as:
Improved Customer Service
Chatbots are popular NLP systems that many businesses use to provide 24/7 support for customers. Unfortunately, chatbots have difficulty understanding customer queries, so NLP helps process such conversations to provide natural responses. Advanced NLP chatbots can go so far as to interpret slang words so that they can answer customers more accurately.
Reduced Costs and Inefficiencies
NLP applications allow you to automate different operations, such as data analysis and customer service. These tasks are often tedious and time-consuming, so you can effectively reduce your costs by streamlining them. Likewise, you can maximise your human resources by freeing your employees’ time for other work.
Ease of Data Analysis
Analysing unstructured text data can be more challenging due to the nuances of natural language. However, you can sift through several data and analyse it quickly with NLP technologies.
NLP can be a valuable tool for your business. By having the right NLP software in your arsenal, you can leverage your data to identify valuable insights and create competitive advantages that will help your business stand out.