Artificial intelligence experts are constantly attempting to create solutions capable of carrying out complex tasks that were once impossible for machines. One of the most crucial things a human mind can achieve is developing and understanding complicated languages. Language is one of the most important reasons people have progressed, which makes it the most talked about topic amongst AI professionals. Over the past two decades, there has been much rapid advancement in natural language processing (NLP). The availability of massive data, advanced technology, and better algorithms, along with a rise in the number of people who wish to connect with machines, have all contributed to the rapid advancement of NLP technology.
Being a member of the human race, we are all able to communicate in languages like English, German, Spanish, or Japanese. Similarly, NLP lets machines talk to people in their language and speeds up other language-related tasks. For example, NLP allows computers to read text, hear speech, and figure out what it means. Machines can analyze more language-based data than people can without getting tired or making mistakes. With the vast amounts of unstructured data being created every minute, from medical records to social media, it will be essential to use automation to analyze text and speech data thoroughly and efficiently. NLP techniques are already used in healthcare, finance, and e-commerce to get information and improve business processes. As machine learning technology improves, there will be more and more information extraction methods.
In this blog post, let’s look at key natural language processing techniques that can be used to get information out of text or unstructured data.
Tokenization – Tokenization is a primary NLP technique that plays an important step in preprocessing text for any NLP application. In this process, a long text string is taken and broken into smaller units called tokens of words, symbols, and numbers. These tokens are building blocks that help to recognize the context while developing an NLP model. Tokenizers mostly use blank space as a separator to create tokens. This technique can be implemented at the word, character, and sub-word levels. For example, the text “They are playing” can be tokenized into ‘They,’ ‘are,’ or ‘playing.’ Word tokenization is the most used technique in NLP among the other techniques used in NLP, such as rule-based white space and dictionary-based tokenization.
Named Entity Recognition (NER) – NER is an NLP technique that helps to identify and extract important elements from text, such as people, locations, organizations, monetary values, or other characteristics. NER can extract key information to understand the text and use it to collect and store important data from huge unstructured datasets. This technique helps businesses to automate the information extraction processes and organize all the data systematically. For example, NER can improve the speed and relevance of search results and recommendation engines by summarizing descriptive texts, reviews, and discussions.
Text Classification – It is a process of organizing huge unstructured text and then structuring it for further analysis. Text classification is an important technique in NLP that solves various business issues. It usually involves structuring business information such as emails, chats conversations, social media, support tickets, documents, and everything in between. Using text classification, businesses can eliminate the need to sort through business data and increase efficiency manually. Sentiment analysis, keyword extraction, and topic modeling are subsets of text classification.
Sentiment Analysis – It is a commonly used NLP technique that helps to understand the emotion of the written text. It is also known as Emotion AI or Opinion Mining. Sentiment analysis helps find positive, negative, or neutral expressed opinions in documents, sentences, texts, film reviews, and social media platforms. This technique works best on subjective text rather than objective data, which are statements or facts without emotions. On the other hand, the subjective text is usually written by humans with feelings and emotions. For instance, Twitter is about expressing opinions on various topics; users utilize this platform to share their opinions and reactions. This NLP technique can identify and delete hate speech from users’ social media platforms to help them from negative reviews.
Keyword extraction – Keyword extraction finds important keywords in a document. It is a widely used NLP technique to extract key information from a sequence of paragraphs or texts. It helps the text analysis process automatically extract important words and expressions from a dataset. The main focus of this technique is to identify phrases that best describe the content of a document which contains key phrases, key terms, key segments, or simply keywords. It is a text analysis NLP technique to gain meaningful insights into any topic. Keyword extraction helps concise the text and extract relevant keywords instead of searching the whole document. Businesses use this keyword Extraction technique to identify the customer’s problems based on their reviews or to search for interesting topics from any contextual item.
Topic Modeling – It helps in the text analysis method, which identifies clustering within the texts to capture the meaning of every word dependent on the context of natural language. Modeling topics is recognizing words within a document or set of data. This technique is helpful in the text extraction process because extracting words from a document takes a lot of time and is more difficult than getting them from topics within the document.
Text Summarization – This technique summarizes a text fluently. Text summarization can extract important data from various documents without reading every word. There are two types of text summarization techniques such as extraction-based and abstraction-based summarization. In extraction-based summarization, some key phrases and words in the document are pulled out to make the summary without changing the original text. While in abstraction-based summarization, new phrases and sentences are created from the original document that captures valuable data. This technique uses paraphrasing and can overcome grammatical inconsistencies found in extraction-based methods.
Lemmatization and stemming – Lemmatization and stems are used in the Text Normalization methods in the field of NLP. These methods are employed to prepare words, text, and documents for additional processing. It is a technical process that refers to text breakdown, tagging, and data restructuring. Both processes lend different valuable data. Lemmatization and stemming are the foundations of derived words, and the only difference between both processes is that lemma is an accurate word, and stem may or may not be a real word.
NLP bridges a crucial gap between software and human understanding for all businesses. NLP enables them to gather a better understanding of market segmentation and shift their focus on targeting customers directly. Organizations are approaching advanced NLP techniques to ensure and invest in products and services for better team outcomes. NLP can be a massive help to any business to save time and automate complex text extraction processes. With advances in computing power, NLP has shifted from a linguistic-based approach to an engineer-based approach which is making significant strides in different industries such as healthcare, retail, manufacturing, finance, education, and many others.