Are you interested in what is natural language processing? Read on as we explain NLP in detail.
The natural language processing market is to reach USD 112.28 billion by 2030. The benefits of investing in NLP are very lucrative.
Developing NLP software requires a deep understanding of AI models based on deep neural networks, statistical techniques, language model development, AI development frameworks, etc.
If you don’t have a professional team with this relevant expertise to take on the complex task, then submit a request for a complimentary discovery call, and one of our tech account managers who managed similar projects will contact you shortly.
Natural Language Processing
Natural Language Processing (NLP) is a discipline of Artificial Intelligence that enables computers to understand and manipulate human language. It evolves from computational linguistics and can comprehend text and voice data much in the same way as humans.
NLP can be divided into two overlapping subfields: natural language understanding (NLU) and natural language generation (NLG).
NLU focuses on determining the intent or meaning of the texts. It involves mapping natural language input to various useful representations.
NLG, on the other hand, is the process of text generation by machines via text data analysis, text planning, phrase generation, etc.
NLP is often used with speech recognition procedures that parse speech to text and vice versa.
Working of Natural Language Processing
NLP uses several methods to analyze human language. The NLP techniques break down human text or speech into smaller parts for easy understanding by computer programs. NLP models find relations between different parts of a language data.
The NLP process involves the following key steps:
Data preprocessing transforms text or words into a format the NLP model can understand. It cleans the data, reduces noise, and improves text quality for better model performance.
Hire expert AI developers for your next project
Data preprocessing includes techniques such as stemming (reducing words to their base), tokenization (breaking text into smaller units), stop word removal (removing words with insignificant meanings), case normalization (removing inconsistencies), handling rare words, etc.
Feature extraction involves transforming data into feature representations or numerical values that NLP algorithms can understand. It uses various techniques to extract meaningful information from the input text.
Some feature extraction techniques include Bag of Words, N-gram representation, Named Entity Recognition (NER), Word Embedding, etc.
Natural Language Processing Techniques
There are two techniques to model natural language processing approaches; traditional machine learning methods and deep learning models.
Traditional Machine Learning techniques include Logistic Regression, Naive Bayes, Decision Trees, etc.
Logistic regression is a supervised machine learning algorithm for binary classification tasks, such as toxicity filtering, spam detection, etc. Logistic regression aims to find a relationship between input features and binary outcomes based on probability values using a logistic function.
Naive Bayes is also a supervised learning algorithm that works on conditional probability. It initially assumes that features are conditionally independent of each other. The Naive Bayes algorithm is commonly used for text classification, sentiment analysis, etc.
Decision trees apply supervised learning by splitting datasets into different sets based on features. Decision trees can be used for classification and regression tasks and work with numerical and categorical data. They can also capture non-linear relationships in data, handle outliers, etc.
Deep Learning models for NLP are convolutional neural networks, recurrent neural networks, autoencoders, etc.
These algorithms work on deep neural network architectures with multiple layers of artificial neurons. DL algorithms learn text patterns and structures from large and complex datasets.
Convolution Neural Network (CNN)
CNNs use convolution layers that apply filters to input data. Convolution neural learning is commonly used for image recognition, computer vision tasks, etc. Convolution layers allow machine learning models to learn hierarchical relationships present in the data.
Some other key components of convolution neural networks include pooling layers, fully connected layers, non-linear activation functions, back-propagation, etc.
Recurrent Neural Network (RNN)
This neural network architecture aims to process sequential and time-series data. Recurrent neural learning creates loops to save and utilize information from previous learning steps compared to forward neural networks.
The key characteristics of recurrent neural networks include recurrent connections, hidden memory states, back-propagation, etc. RNNs suffer from the vanishing gradient problem resulting in difficulties in learning long-term dependencies.
Advanced RNNS such as LSTMs (Long Short-Term Memory Networks) and GRUs (Gated Recurrent Units) help to overcome such limitations.
RNNs are largely used for NLP tasks like machine translation, speech recognition, language modeling, etc.
Hire expert AI developers for your next project
1,200 top developers
us since 2016
Autoencoders are a type of neural network architecture for unsupervised learning and dimension reduction. They train a model to reconstruct its input and aim to minimize the reconstruction error between input and output.
Applications of autoencoders are many, including anomaly detection and construction of different outputs like summary, translation, etc.
Use Cases of NLP
NLP can accomplish a variety of linguistic tasks. Some of these use cases include the following:
It is a machine learning-based approach to recognizing emotions in textual or speech data. Generally, the input to a sentiment classification model is textual data, and the output is the probability of the sentiment being happy, angry, neutral, etc.
AI achieves 85% accuracy in sentiment identification, compared to other methods.
Sentiment analysis has many applications. For example, businesses use it to gauge customer feelings after the use of a product/service by analyzing their reviews.
According to a survey, 83% of businesses have adopted technology to analyze customer sentiments from their reviews or social media. The adoption of such tools shows the strongest correlation with the company’s revenue growth.
Similarly, social media platforms try to keep online spaces safe for everyone by monitoring online content for various emotions. ML algorithms flag texts and visuals that show harmful emotions like threats, harassment, etc.
According to Meta AI, NLP-based AI tools help them proactively detect and remove 88.8 percent of hate speech content from their online platforms.
The process extracts unique entities present in a text and categorizes them. The predefined categories can include person names, organizations, locations, etc.
The input to the Named-Entity recognition method is the text, and the output consists of entity names with start and end positions.
Named-Entity recognition has many applications, such as question-answering systems, information retrieval applications, etc. NEC improves the overall search feature, offers correct answers, tackles misinformation, etc.
Spam detection is a famous classification use case in natural language processing. Spam detection algorithms take text as input and output the probability of it being scam content.
Spam detection finds many applications such as spam email filtering, fraud detection, moderation of online platforms, etc. The global Anti-spam software market is expected to reach USD 22.03 billion by 2030. Artificial Intelligence is significantly driving market growth.
Gmail uses such an algorithm that considers the sender’s name, address, email content, etc., and filters emails with a high probability of being a scam into a separate spam folder. Google claims it catches 99.9 percent of spam emails on Gmail via AI adoption.
These sophisticated NLP algorithms generate text similar to humans. They are formally known as natural language generation algorithms. Such algorithms use complicated machine learning architectures like LSTM, GPT, etc.
Text generation models train and fine-tune to generate different types of text like summaries, essays, blog posts, etc., and in various contexts.
OpenAI’s GPT (generative pre-trained transformer) models are prime examples of NLP models trained to understand human language and provide text outputs in response to inputs.
The applications of text generation NLP are many, such as text auto-completion, text generation, conversational chatbots, virtual assistants, etc. ChatGPT by OpenAI is a popular example of a text generation-based NLP application.
NLP is used to summarize lengthy texts into short summaries. The model is trained to highlight important information, evaluate factual consistencies, and produce accurate summarised content.
NLP summarization can be divided into two types:
- Extractive Summarization extracts important parts or sentences from the text and combines them to form a summary.
- Abstractive Summarization generates a summary using paraphrasing. It might not use the same words or sentences as in the original text, much like abstract writing.
IBM Watson Discovery is an AI tool that leverages NLP and machine learning to extract relevant information from research papers and generate summaries, highlighting key concepts, conclusions, etc.
Hire expert AI developers for your next project
With this tool, researchers can save significant time while analyzing and understanding comprehensive research papers. Advanced language understanding capabilities allow the Watson Discovery tool to work with domain-specific and scientific terminologies effectively.
NLP Machine translation refers to automatic translation between two languages. The input is a text in a source language, and the output is text translated into the target language.
Machine translation of speech and voice finds uses in several areas, like global communication, software localization, multilingual app experience, multilingual customer support, etc.
In the US, the market size for translation services is USD 9.9 billion. 65% of people prefer online content in their own language, moreover, 40% of global consumers refuse to buy in another language even if they understand the other language.
Google Translate and Baidu Speech are the top examples of machine translation applications.
Popular NLP Development Tools and Libraries
Many tools and libraries are available that accelerate NLP implementation to process language-rated tasks. Some commonly used AI tools by data scientists and AI developers include the following:
Natural Language Toolkit (NLTK)
It is a popular Python programming language library for natural language processing. It offers a range of tools for NLP tasks like tokenization, lemmatization, parsing, etc. It also provides various lexical resources.
CoreNLP library is developed by Standford. It offers features for tokenization, POS tagging, NER, sentiment analysis, etc. Developers can use CoreNLP via a Java API or a command-line interface.
TensorFlow is a popular deep-learning framework that offers powerful tools to develop and train complex neural networks. Using TensorFlow and PyTorch, developers can implement complex models, such as sequence-to-sequence, transformers, etc.
This library provides tools for working with vast text data for tasks like topic modeling, document similarity analysis, document clustering, etc. It accelerates the implementation of popular NLP models like Latent Semantic analysis, Words2Vec, etc.
SpaCy is another popular Python library for NLP. It offers ready-to-use components for tasks like part-of-speech tagging, named-entity recognition, parsing, etc. SpaCy is known for its high performance.
Natural language processing offers several benefits to businesses and users alike. Some include the following:
- Ability to analyze vast amounts of structured and unstructured data. 48% of businesses stated in a survey that they use machine learning and AI tools to handle data quality issues.;
- Apply sentiment analysis to data and derive valuable insights related to business sales, marketing, customer experience, etc. 75% of customer experience leaders are enhancing their personalization at scale efforts.
- Automate tasks, such as text generation, answering customer queries, going through large text and speech data, etc., and reduce cost, increase business efficiency, etc. According to Mickensy, 31% of businesses have fully automated at least one function.
Although natural language processing has made significant advancements, it still faces some challenges and limitations, such as the following:
- Insufficient understanding of complex contexts in human communication;
- Lack of background knowledge;
- Sensitive to data quality and real-world biases;
- Huge computation requirements;
- Inability to handle out-of-domain words.
Planning an NLP Project?
Artificial intelligence technology is continuously advancing. Businesses cannot afford to undermine NLP if they want to stay ahead in the market. However, natural language processing, like other areas of artificial intelligence, is a complex field.
You require expert developers with skills in machine learning algorithms, handling of training data, Python programming language, AI development tools, etc., to design and build NLP applications.
If you do not have such talent on your team, we suggest you partner with a credible software development company like DevTeamSpace with experience in building market-competitive AI solutions.
All developers at DevTeam.Space are motivated and experts in the latest technologies. Moreover, all our developers follow an AI-powered agile development process.
If you want to know more about how we can help you build an NLP application, send us your initial project specifications via this quick form. One of our account managers will get back to you soon.
FAQs on Natural Language Processing
Natural language processing is a sub-field of artificial intelligence, overlapping machine learning methods, computer science, and computational linguistics focusing on interactions between machines and humans. Based on deep learning methods and statistical natural language processing, computational models understand human language, generate human-like text, perform sentiment analysis, translate one language into another, etc.
Some examples of language-related tasks in daily life using NLP include conversational chatbots, voice assistants, text summarizers, fraud detection filters, etc.
NLP tasks involve several linguistic and statistical methods to analyze and process human language models, such as text processing, word sense disambiguation, part-of-speech tagging, morphological analysis, parsing, semantic analysis, statistical modeling, natural language generation, etc. Statistical NLP methods are often used together with speech recognition software.