13 Natural Language Processing Techniques to Unlock Smarter AI Models

Have you ever had a conversation with a smart device? You asked your phone a question, which quickly returned an answer. Or you told a home assistant to play a song, and it immediately complied. Natural language processing can help machines understand human language, and the more sophisticated it becomes, the more conversational and intelligent machines become. Natural language processing techniques can help your AI models accurately and efficiently understand, analyze, and generate human language at scale. This article explores machine learning frameworks and natural language processing techniques to help you build more brilliant AI models that can easily handle human language.

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What is Natural Language Processing (NLP)?

Natural language processing, or NLP, is an AI-powered technology that enables computers to understand, interpret, and manipulate human language. NLP combines computational linguistics, which focuses on human language from a statistical and algorithmic perspective, with machine learning, an intensive learning approach.

As the name implies, deep learning utilizes neural networks to simulate the way the human brain functions. The more data these algorithms process, the better they get at understanding human language.

Why Does Natural Language Processing (NLP) Matter?

NLP is an integral part of everyday life. It is becoming increasingly so as language technology is applied to diverse fields, such as retailing (for instance, in customer service chatbots) and medicine (for interpreting or summarizing electronic health records). Conversational agents, such as Amazon’s Alexa and Apple’s Siri, utilize NLP to listen to user queries and find relevant answers.

Advanced NLP Applications in AI and Tech

The most sophisticated such agents, such as GPT-3, which was recently opened for commercial applications, can generate sophisticated prose on a wide variety of topics as well as power chatbots that are capable of holding coherent conversations. Google uses NLP to improve its search engine results, and social networks like Facebook use it to detect and filter hate speech.

Components of NLP

Natural Language Processing is not a monolithic, singular approach; instead, it is composed of several components that contribute to the overall understanding of language. The main components that NLP strives to understand are:

• Syntax
• Semantics
• Pragmatics
• Discourse

Syntax

Syntax pertains to the arrangement of words and phrases to create well-structured sentences in a language. Example: Consider the sentence "The cat sat on the mat." Syntax involves analyzing the grammatical structure of this sentence, ensuring that it adheres to the grammatical rules of English, such as:

• Subject-verb agreement
• Proper word order

Semantics

Semantics is concerned with understanding the meaning of words and how they create meaning when combined in sentences. Example: In the sentence "The panda eats shoots and leaves," semantics helps distinguish whether the panda eats plants (shoots and leaves) or is involved in a violent act (shoots) and then departs (leaves), based on the meaning of the words and the context.

Pragmatics

Pragmatics deals with understanding language in various contexts, ensuring that the intended meaning is derived based on the situation, the speaker’s intent, and shared knowledge. Example: If someone says, "Can you pass the salt?" Pragmatics involves understanding that this is a request rather than a question about one's ability to pass the salt, interpreting the speaker’s intent based on the dining context.

Discourse

Discourse focuses on the analysis and interpretation of language beyond the sentence level, considering how sentences relate to each other in texts and conversations. Example: In a conversation where one person says, "I’m freezing," and another responds, "I’ll close the window," discourse involves understanding the coherence between the two statements, recognizing that the second statement is a response to the implied request in the first.

Understanding these components is crucial for anyone delving into NLP, as they form the backbone of how NLP models interpret and generate human language.

What is Natural Language Processing (NLP) Used For?

NLP is utilized for a wide range of language-related tasks, including answering questions, categorizing text in various ways, and engaging in conversations with users.

11 Functions That NLP Can Solve

1. Sentiment Analysis

Sentiment analysis is the process of classifying the emotional intent of text. Generally, the input to a sentiment classification model is a piece of text, and the output is the probability that the sentiment expressed is positive, negative, or neutral. Typically, this probability is based on either:

• Hand-generated features
• Word n-grams
• TF-IDF features
• The use of deep learning models to capture both long- and short-term dependencies

Sentiment analysis is used to classify customer reviews on various online platforms, as well as for niche applications such as identifying signs of mental illness in online comments.

2. Toxicity Classification

Toxicity classification is a branch of sentiment analysis that aims not only to classify hostile intent but also to categorize specific types of content, such as threats, insults, obscenities, and hatred towards particular identities. The input to such a model is text, and the output is generally the probability of each class of toxicity.

Toxicity classification models can be used to moderate and improve online conversations by silencing offensive comments, detecting hate speech, or scanning documents for defamation.

3. Machine Translation

Machine translation automates translation between different languages. The input to such a model is text in a specified source language, and the output is the text in a specified target language. Google Translate is the most famous mainstream application. Such models are used to improve communication between people on social media platforms such as:

• Facebook
• Skype

Practical approaches to machine translation can distinguish between words with similar meanings. Some systems also perform language identification; that is, classifying text as being in one language or another.

4. Named Entity Recognition

Named entity recognition aims to extract entities in a piece of text into predefined categories such as:

• Personal names
• Organizations
• Locations
• Quantities

The input to such a model is generally text, and the output is the various named entities along with their start and end positions. Named entity recognition is helpful in applications such as:

• Summarizing news articles
• Combating disinformation

5. Spam Detection

Spam detection is a prevalent binary classification problem in NLP, where the goal is to classify emails as either spam or not spam. Spam detectors take as input an email text along with various other subtexts like the title and the sender’s name. They aim to output the probability that the mail is spam.

Email providers like Gmail utilize such models to enhance the user experience by detecting unsolicited and unwanted emails and directing them to a designated spam folder.

6. Grammatical Error Correction

Grammatical error correction models encode grammatical rules to correct the grammar within text. This is primarily viewed as a sequence-to-sequence task, where a model is trained on an ungrammatical sentence as input and a grammatically correct sentence as output. Online grammar checkers that utilize these tools to enhance the writing experience for their users:

• Grammarly
• Microsoft Word

7. Topic Modeling

Topic modeling is an unsupervised text mining task that analyzes a corpus of documents to identify abstract topics within that corpus. The input to a topic model is a collection of documents, and the output is a list of issues that define words for each topic, as well as the assignment proportions of each subject in a document.

Latent Dirichlet Allocation (LDA), one of the most popular topic modeling techniques, tries to view a document as a collection of topics and a topic as a collection of words. Topic modeling is being used commercially to help lawyers find evidence in legal documents.

8. Text Generation

Text generation, more formally known as natural language generation (NLG), produces text that’s similar to human-written text. Such models can be fine-tuned to make text in different genres and formats, including tweets, blogs, and even computer code. Text generation has been performed using:

• Markov processes
• LSTMs
• BERT
• GPT-2
• LaMDA
• Other approaches

9. Autocomplete

Autocomplete predicts the next word, and autocomplete systems of varying complexity are used in chat applications like WhatsApp. Google uses autocomplete to predict search queries. One of the most famous models for autocomplete is GPT-2, which has been used to:

• Write articles
• Song lyrics
• Much more

10. Chatbots

Chatbots automate one side of a conversation while a human conversant generally supplies the other side. They can be divided into the following two categories:

• Database query: We have a database of questions and answers, and we would like a user to query it using natural language.
• Conversation generation: These chatbots can simulate dialogue with a human partner.

Some are capable of engaging in wide-ranging conversations. A high-profile example is Google’s LaMDA, which provided answers so human-like that one of its developers was convinced it had feelings.

11. Information Retrieval

Information retrieval finds the documents that are most relevant to a query. This is a problem every search and recommendation system faces. The goal is not to answer a particular query but to retrieve, from a collection of documents that may be numbered in the millions, a set that is most relevant to the query. Document retrieval systems mainly execute two processes:

• Indexing
• Mtching

In most modern systems, indexing is done by a vector space model through Two-Tower Networks, while matching is done using similarity or distance scores. Google recently integrated its search function with a multimodal information retrieval model that works with text, image, and video data.

12. Summarization

Summarization is the task of shortening text to highlight the most relevant information. Researchers at Salesforce have developed a summarizer that also evaluates factual consistency to ensure the accuracy of its output. Summarization is divided into two method classes:

• Extractive summarization focuses on extracting the most important sentences from a long text and combining these to form a summary. Typically, extractive summarization scores each sentence in an input text and then selects a subset of sentences to create the summary.
• Abstractive summarization produces a summary by paraphrasing. This is similar to writing the abstract that includes words and sentences that are not present in the original text. Abstractive summarization is usually modeled as a sequence-to-sequence task, where the input is a long-form text and the output is a summary.

13. Question Answering

Question answering deals with answering questions posed by humans in a natural language. One of the most notable examples of question answering was Watson, which in 2011 played the television game show Jeopardy against human champions and won by substantial margins. Generally, question-answering tasks come in two flavors:

• Multiple-choice question: The multiple-choice question problem consists of a question and a set of possible answers. The learning task is to pick the correct answer.
• Open domain: In open-domain question answering, the model provides answers to questions in natural language without any options provided, often by querying a large number of texts.

13 Natural Language Processing Techniques Every Data Scientist Should Know

1. Tokenization: Splitting Texts into Manageable Pieces

Tokenization is a primary and straightforward NLP technique for natural language processing. Tokenization is an essential step while preprocessing text for any NLP application. A long-running text string is broken into smaller units called tokens, which constitute words, symbols, numbers, etc.

These tokens are the building blocks and help understand the context when developing an NLP model. Most tokenizers use the “blank space” as a separator to form tokens. Based on the language and purpose of the modeling, there are various tokenization techniques used in NLP–Rule-Based Tokenization:

• White Space Tokenization
• Spacy Tokenizer
• Subword Tokenization
• Dictionary Based Tokenization
• Penn Tree Tokenization

2. Stemming and Lemmatization: Reducing Words to Their Roots

After tokenization, the next preprocessing step is either stemming or lemmatization. These techniques generate the root word from the different existing variations of a word.

For example, the root word “stick” can be written in many different variations, like:

• Stick
• Stuck
• Sticker
• Sticking
• Sticks
• Unstick

Stemming and lemmatization are two different ways to identify a root word. Stemming works by removing the end of a word. This NLP technique may or may not work depending on the word. For example, it would work on “sticks,” but not “unstick” or “stuck.” Lemmatization is a more sophisticated technique that uses morphological analysis to find the base form of a word, also called a lemma.

3. Stop Words Removal: Filtering Out Unnecessary Noise

Stop word removal is another NLP preprocessing step that removes filler words to allow the AI to focus on words with meaning. This includes conjunctions such as “and” and “because,” as well as prepositions such as “under” and “in.” By removing these unhelpful words, NLP systems are left with less data to process, allowing them to work more efficiently. It isn’t necessary for every NLP use case, but it can help with text classification.

4. TF-IDF: Determining Word Relevance in a Document

TD-IDF, which stands for term frequency-inverse document frequency, is a statistical technique that determines the relevance of a word to one document in a collection of documents. It looks at two metrics:

• The number of times a word appears in a given document
• The number of times the same word appears in a set of documents

If a word is common in every document, it won’t receive a high score, even if it occurs many times. But if a word frequently repeats in one document while rarely appearing in the rest of the documents in a set, it will rank high, suggesting it is highly relevant to that one document in particular.

5. Keyword Extraction: Automatically Identifying Important Terms

Keyword extraction is a technique that skims a document, ignoring the filler words and honing in on the critical keywords. It automatically extracts the most frequently used and essential words and phrases from a document, helping to summarize it and identify what it’s about.

This is highly useful for any situation in which you want to identify a topic of interest in a textual dataset, such as whether a problem comes up repeatedly in customer emails.

6. Word Embeddings: Converting Text into Numerical Vectors

Machine learning and deep learning models require numerical input, making it essential to convert textual data into numerical form for tasks such as classification or regression. One of the most effective NLP techniques for this transformation is word embedding.

What Are Word Embeddings?

Word embeddings are numerical vector representations of words, learned to map semantically similar words to nearby points in an n-dimensional space. These vectors help models understand linguistic context and word relationships more naturally than simple one-necessary frequency-based methods.

For example, in a 3-dimensional vector space, the word “walking” would be located closer to “walked” than to “king”, because they share the same root and meaning.

Similarly, embeddings can capture relationships like:

King - Man + Woman ≈ Queen

Word embeddings can either be:

• Pretrained (e.g., trained on massive datasets like Wikipedia)
• Learned from scratch (specific to your custom dataset)

Popular Word Embedding Techniques

• TF-IDF (Term Frequency–Inverse Document Frequency): Measures word importance relative to the document and corpus; lacks context or semantics.
• CountVectorizer: Converts text to a matrix of token counts; functional but doesn't capture meaning.
• Word2Vec: A neural network-based model that learns word associations from large text corpora.
• GloVe (Global Vectors for Word Representation): Combines global matrix factorization and local context windowing to capture meaning.
• ELMo (Embeddings from Language Models): Contextual embeddings considering the entire sentence structure.
• BERT (Bidirectional Encoder Representations from Transformers): Deep contextualized embeddings based on transformer architecture.

Focus: Word2Vec

Word2Vec is a popular word embedding method that uses a shallow neural network to learn vector representations of words.

It operates in two main modes:

1. CBOW (Continuous Bag of Words)

• Input: Context words surrounding a target word
• Output: The predicted target word
• Example: In the sentence “The day is bright and sunny”, CBOW uses “The day is bright” to predict “sunny”

2. Skip-Gram

• Input: A single target word
• Output: The surrounding context words
• Example: Using “sunny” as input, the model tries to predict words like “bright”, “and”, etc.

Choosing the Right Word Embedding Technique for Your NLP Task

Each word is typically represented as a one-hot encoded vector, which the model transforms into a dense, low-dimensional vector. Over time, the model learns to group similar words closer together in the vector space, based on their contextual usage.

Word embeddings are crucial in enabling machines to understand text syntactically and semantically. Choosing the proper embedding technique depends on your task complexity, dataset size, and desired level of language understanding.

7. Sentiment Analysis: Understanding Emotions Behind Text

Sentiment Analysis, also known as emotion AI or opinion mining, is one of the most essential NLP techniques for text classification. The goal is to classify text like a tweet, news article, movie review, or any text on the web into one of these 3 categories:

• Positive
• Negative
• Neutral

Sentiment Analysis is most commonly used to mitigate hate speech on social media platforms and identify distressed customers based on negative reviews.

8. Topic Modelling: Discovering Hidden Themes in Texts

Topic modeling is a technique that scans documents to find themes and patterns within them, clustering related expressions and word groupings to tag the set. It’s an unsupervised machine learning process, meaning it doesn’t require the documents it is processing to have previously been categorized by humans.

9. Text Summarization: Reducing Text to its Essentials

This NLP technique is used to concisely and briefly summarize a text fluently and coherently. Summarization helps extract helpful information from documents without reading word by word. This process is very time-consuming if done by a human, automatic text summarization reduces the time radically.

There are two types of text summarization techniques.

• Extraction-Based Summarization: In this technique, some key phrases and words in the document are pulled to make the summary. No changes to the original text are made.
• Abstraction-Based Summarization: In this text summarization technique, new phrases and sentences from the original document capture the most helpful information. The language and sentence structure of the summary are not the same as the original document because this technique involves paraphrasing.

We can also overcome the grammatical inconsistencies found in extraction-based methods.

10. Named Entity Recognition: Extracting Key Information

Named entity recognition (NER) is a type of information extraction that locates and tags “named entities” with predefined keywords such as names, locations, dates, events, and more. In addition to tagging a document with keywords, NER tracks how often a named entity is mentioned in a given dataset.

NER is similar to keyword extraction, but the extracted keywords are put into predefined categories. NER can be used to identify how often a specific term or topic is mentioned in a given data set. For example, it might be used to identify that a particular issue, tagged as a word like “slow” or “expensive,” comes up repeatedly in customer reviews.

11. Morphological segmentation: Understanding the Building Blocks of Words

Morphological segmentation is splitting words into the morphemes that make them up. A morpheme is the smallest unit of language that carries meaning. Some words, such as “table” and “lamp,” only contain one morpheme.

But other words can contain multiple morphemes. For example, “sunrise” contains two morphemes:

• Sun
• Rise

Like stemming and lemmatization, morphological segmentation can help preprocess input text.

12. Text classification: Organizing Text Data into Categories

Text classification is an umbrella term for any technique that organizes large quantities of raw text data. Sentiment analysis, topic modeling, and keyword extraction are all different types of text classification, and we’ll discuss them shortly. Text classification essentially structures unstructured text data, preparing it for further analysis. It can be used on nearly every text type and helps with several organization and categorization applications.

In this way, text classification is an essential part of natural language processing, used to help with everything from detecting spam to monitoring brand sentiment. Some possible text classification applications include:

• Grouping product reviews into categories based on sentiment.
• Flagging customer emails as more or less urgent.
• Organizing content by topic.

13. Parsing: Understanding the Grammar of Texts

between languages. Machine translation tools, like Google Translate, can quickly translate text so businesses can communicate with customers from different countries.

With NLP, companies can translate large volumes of text to help with customer support, data mining, and even publishing multilingual content. For example, if a company receives a negative review written in Spanish, it can use NLP technology to translate it into English. This can help the organization quickly understand the customer’s concerns and respond to them to improve customer satisfaction.

How NLP Improves Information Retrieval

NLP can improve information retrieval processes to help organizations quickly access and retrieve data from unstructured databases. Most business data is unstructured, meaning it doesn’t fit neatly into a spreadsheet. For instance, customer feedback, employee reviews, and social media comments are all unstructured data types that contain valuable information.

NLP can help organizations analyze this unstructured data, extract the needed information, and make it available in a structured format that is easier to work with. This can help companies respond to customer inquiries, improve products and services, and make data-driven business decisions.

How NLP Boosts Sentiment Analysis

Sentiment analysis, or opinion mining, uses NLP algorithms to detect human emotion in written text. The technology can help organizations make sense of large volumes of customer feedback

Parsing is the process of figuring out the grammatical structure of a sentence, determining which words belong together as phrases and which are the subject or object of a verb. This NLP technique offers additional context about a text to help with processing and analyzing it accurately.

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Natural Language Processing Applications

Natural language processing can help organizations translate text to understand what their audiences are saying about a:

• Brand
• Product
• Service

For example, if a company wants to learn how customers feel about its new product, it can use sentiment analysis to analyze online reviews, social media comments, and blog posts. This can reveal general information about product sentiment and specific details about what customers like and dislike to help organizations make improvements.

How NLP Improves Question Answering

NLP’s question-answering capability can help organizations respond to customer inquiries more efficiently. Instead of sifting through endless databases or documents to find answers, NLP-powered tools can quickly locate the necessary information and deliver it to the user. This can improve self-service customer experiences by helping buyers find the answers they need without waiting for a human to respond.

For instance, if a customer has a question about how to use a specific feature of a software product, they can type their inquiry into the search bar of the software’s support page. An NLP tool can instantly return the relevant information to the customer, improving their experience and reducing the likelihood that they will become frustrated and reach out to customer service.

How NLP Powers Chatbots

One of the most popular recent applications of NLP technology is ChatGPT, the trending AI chatbot that’s probably all over your social media feeds. ChatGPT is fueled by NLP technology, using a multi-layer transformer network to generate human-like written responses to inquiries submitted in natural human language.

ChatGPT uses unsupervised learning, which means it can generate responses without being told the correct answer. It is an exciting step forward in applying NLP technology for businesses and individuals, with many saying it can rival Google. Possible uses for ChatGPT include:

• Customer service
• Translation
• Summarization
• Content writing

How NLP Boosts Customer Experience Analytics

Using NLP for social listening and customer review analysis can give tremendous insight into what customers think and say about a brand and its products.

With sentiment analysis and text classification, companies can:

• Understand general sentiment about the brand
• Does the public feel positively or negatively about us?
• Identify what customers like and dislike about a service or product.
• Learn what new products customers might be interested in.
• Know which products to scale and which to pull back on.
• Discover insights that can be used to improve customer experience and boost customer satisfaction.

How Sentiment Analysis Drives Data-Backed Product Decisions in Real Time

For example, spicy chocolate brand Shock-O just released a new Popping Jalapeno Chocolate and wants to know whether customers like it. Shock-O can use an NLP-powered tool to analyze customer sentiment and learn what people are saying about the Popping Jalapeno Chocolate, whether they speak about it positively or negatively, and what themes come up repeatedly in reviews of this product.

This information can then determine whether to continue producing Popping Jalapeno Chocolate, increase or decrease its production, make it spicier or less spicy, etc.

How NLP Improves Customer Service

90% of customers believe receiving an immediate response is essential when they have questions. Yet human customer service representatives are limited in availability and bandwidth. This is just one reason why NLP-powered chatbots are growing in popularity.

By understanding and analyzing customer inquiries properly, chatbots can offer the necessary answers to questions, helping to improve customer satisfaction while cutting down on agents’ workload. NLP can also process and analyze customer service surveys and tickets to understand customers’ issues better, what they’re happy with, what they’re unhappy with, and more. All of this serves as crucial data for boosting customer happiness, which will, in turn, increase customer retention and improve word-of-mouth.

How NLP Aids Recruitment

HR professionals spend countless hours reviewing resumes to identify suitable candidates. NLP can make this process much more efficient by taking over the screening process and analyzing resumes for specific keywords. For example, you might set up an NLP system to flag any resume that uses the word “Python” or “leadership” for a human to review later.

This can increase the likelihood of finding strong candidates, helping an organization fill open positions more quickly and with better talent. It can also free up HR professionals’ time to focus on tasks requiring more strategic thinking.

Challenges and Considerations of Natural Language Processing Techniques

Despite its advancements, the domain of NLP has several challenges that include:

• Context Understanding: Often, understanding and capturing the context of a particular conversation or text is challenging, but this is crucial for accurately interpreting the models.
• Ambiguity: The world's many human languages are often ambiguous, with different languages containing words that have multiple meanings depending on the context.
• Data Privacy: Ensuring the privacy of user data is particularly challenging when processing large volumes of personal text data.
• Language Diversity: This is considered the most challenging task, which involves developing machine learning models that accurately work for various languages and dialects.

Future Trends in Natural Language Processing (NLP)

The future of NLP in this era of Artificial Intelligence (AI) is auspicious, with several trends shaping its evolution, including:

• Multimodal NLP: Integrating text, speech, and visual data for a more comprehensive understanding of context and meaning.
• Explainable AI: Developing models that provide clear explanations for their decisions and outputs.
• Low-Resource Language Processing: Improving NLP capabilities for languages with limited available data.
• Personalization: Tailoring NLP applications to individual user preferences and behaviors for more personalized experiences.

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