Natural language processing, or NLP, is about designing computer programs that can understand and respond to human languages. The main goal of NLP and ML is to translate between human languages and machine languages.
NLP focuses on enabling computers to work with text or voice data from human languages. This includes understanding the complex meanings and relationships in sentences written in languages like English and Chinese. NLP programs apply algorithms to process huge amounts of natural language data to perform helpful tasks.
Some common applications that use NLP technology are:
- Machine translation: Automatically translating between human languages like turning Chinese sentences into English.
- Text classification: Assigning categories or tags to pieces of text, like detecting positive or negative emotions.
- Speech recognition: Transcribing spoken human audio into written format. For example converting speech into text queries a virtual assistant can understand.
Many AI systems we interact with daily rely on NLP. This includes chatbots, search engines, voice assistants like Alexa or Siri, and more. The field of natural language artificial intelligence has made good progress recently due to breakthroughs in deep learning. However more advancement is still needed to reach human-level comprehension of languages.
What is NLP
Natural language processing (NLP) tools are becoming popular across industries to understand human language data. Many tech top NLP companies like Google, Microsoft, and startups now provide NLP platforms and services.
Key tools for AI NLP include:
- Azure Natural Language Processing: A cloud service on Microsoft Azure providing pre-built NLP AI models for text analytics.
- John Snow Labs NLP: Specializes in healthcare NLP solutions for patient data and medical research.
- Google Natural Language AI: Offers NLP models via APIs to analyze text, extract information, classify content and more.
- OpenAI NLP: Develops conversational AI systems and publishes research to advance language AI capabilities.
- Cohere NLP: An NLP API with large pre-trained models aimed at developers without machine learning expertise.
- IBM Watson NLP: IBM’s suite of NLP tools and services for applications across industries.
Top applications of these AI NLP models are:
- Sentiment analysis: Automatically gauging emotions and opinions in customer feedback.
- Information retrieval: Enabling keyword based search in large document collections.
- Clinical documentation: Extracting insights from patient health records and notes.
- NLP Chatbots: Allowing conversational interactions with data and services.
The increasing power of deep learning is enabling more human-like NLP abilities. However natural language generation and understanding complete language remain challenging areas of ongoing research in academia and industry. Advancing interpretable and unbiased artificial intelligence language processing also warrants more attention. Overall, NLP promises to become integral across domains dealing with text and speech.
From Statistical Models to Deep Learning: The Evolution of NLP
The goal of NLP research is to teach computers to understand and generate human languages. NLP powers applications like voice assistants, translation tools, and chatbots. The field has advanced from early rule-based ideas to the latest deep learning powered language AI.
First NLP systems used hand-written rules and dictionaries to analyze sentence structures. But these could not handle the complexity of how people naturally communicate.
Next came statistical NLP, applying basic machine learning to learn patterns from large volumes of actual text instead of manually coded rules. Speech recognition also evolved from statistical models to neural networks. Multimodal language processing AI combining speech, text and visuals also emerged.
But a core challenge remained – meaning. How could systems truly understand language semantics the way humans do?
Deep learning brought major progress on this front. Word embedding methods like Word2Vec used neural networks to encode semantic meaning from word usage across texts. Long short-term memory (LSTM) recurrent neural networks advanced sequence modeling abilities critical for language.
In recent years, transformer models have driven stunning improvements via attention mechanisms to understand context. Pre-trained on huge data, models like BERT, GPT-3 and T5 can transfer learned knowledge to downstream NLP tasks through fine-tuning.
The latest approaches apply self-supervised multimodal learning across both unlabeled text and speech or images found online. As available data and compute scale, NLP in artificial intelligence can match more flexible, human-like language abilities. Deep neural networks now power most advances in conversational NLP, comprehension and content creation.
Machine Learning Models for Core NLP Tasks
Modern NLP uses deep learning models like neural networks to understand text and speech. These advanced AI NLP machine learning models enable computers to process human languages.
Machine Translation
Machine translation automatically converts text or speech from one language to another. Rule-based systems build translation rules manually. Statistical machine translation (SMT) applies probability and AI to translate based on volumes of parallel bilingual texts. Neural machine translation uses deep learning for more fluent context-aware translations.
Translation quality depends greatly on training data volume and variety. Performance metrics include BLEU, which compares machine to human reference translations. Vocabulary variation, ambiguous meanings, regional dialects and complex grammar still challenge AI translators.
Text Classification
Text classification assigns predefined tags or categories to texts like spam filtering or sentiment analysis. Common methods include logistic regression, support vector machines (SVMs) and neural networks like LSTMs.
The classifier is trained on labelled example texts then predicts labels for unseen texts. Training data quality greatly impacts accuracy. Real-world uses include analyzing customer feedback, moderating offensive content, recommender systems and document organization.
Speech Recognition
Speech recognition transcribes human speech into text. Traditional approaches model audio signals as numeric features to match text with statistical models like hidden Markov models (HMMs).
Deep learning methods now dominate using convolutional and recurrent neural networks to translate speech directly to text. Hybrid HMM-DNN models retain robust statistical foundations enhanced by deep networks. Techniques like beam search improve decoding efficiency.
Accuracy still drops with background noise and identifying voices remains challenging. Uses include automated speech captioning, voice assistants, hands-free computing and speech analytics.
Ongoing research tackles foundational language challenges like inference, open-domain questioning and low-resource translation to expand AI and NLP capabilities for both specialist and everyday applications.
Real-World Applications of NLP Across Industries
Natural language processing (NLP) is being widely adopted to understand and generate human language across sectors. Major areas applying NLP solutions include:
Healthcare
NLP enables analyzing complex medical texts and patient documentation to improve diagnoses and treatment. Healthcare NLP companies identify key information in records to support decision-making and personalized care. Applications include medical speech recognition, clinical documentation improvement, diagnostic assistants, and hospital NLP based chatbots.
Finance
Banks apply NLP for use cases like sentiment analysis of market news, algorithmic trading through language cues, catching fraud from customer interactions, business intelligence from earnings calls, and streamlining client onboarding. Other uses cover credit/insurance risk assessment and personalized recommendations.
E-Commerce and Retail
Consumer-facing NLP powers next-word keyboard suggestions, NLP models for chatbots for customer service, product tagging, search engines, recommendation systems powered by reviews, targeted advertising etc. Behind the scenes, NLP aids price optimization, demand forecasting, inventory management and supply chain analytics.
Legal
NLP facilitates reviewing and analyzing large troves of legal documents and case law to aid research, due diligence and discovery. Contract analytics, e-discovery tools and solutions to anonymize sensitive data also employ NLP. It further enables NLP and chatbots to offer basic legal assistance.
NLP Insurance
Insurance companies are using NLP across different processes. NLP tools help transcribe call recordings with customers to improve service. Analyzing client emails and claims reports with NLP also helps predict risk and set fair premiums. AL NLP further powers chatbots for common customer queries and automates administrative paperwork. Overall AI and natural language processing aids insurers to reduce costs and offer competitively priced policies. As language AI keeps advancing, even more insurance workflows will benefit.
As NLP accuracy keeps improving with advances in transformer networks and availability of multimodal labeled data, applications will only expand. Most customer-facing and document-heavy verticals stand to benefit greatly from language-focused AI capabilities – both from cost and revenue standpoints as well as augmenting human capabilities.
NLP Frameworks Like spaCy, Stanford CoreNLP and NLTK
NLP frameworks are toolkits that make building natural language processing systems easier. They provide pre-made components for common NLP tasks so developers don’t have to code everything from scratch. Popular options include spaCy, Stanford CoreNLP, and NLTK.
spaCy
spaCy is a Python library focused on performance and production use. Key features include pre-trained statistical models for prediction tasks like part-of-speech tagging, labeled dependency parsing, named entity recognition and text classification. It is optimized for CPU usage allowing fast NLP ML analysis. The streamlined API also suits building production grade applications.
Stanford CoreNLP
Stanford CoreNLP is a Java toolkit developed over decades of NLP research at Stanford University. It supports common text analysis uses like tokenization, part-of-speech tagging, named entity recognition, sentiment analysis and relation extraction. User-friendly annotation graphics and detailed documentation aid utilizing CoreNLP models.
NLTK
NLTK (Natural Language Toolkit) is a pioneer Python library for teaching and prototyping with NLP. It offers simplified access to over 50 corpora and trained models for text processing. NLTK also incorporates datasets for training new models. While less scalable than industrial applications, NLTK facilitates quick implementation of natural language processing AI concepts for analysis and learning.
These frameworks illustrate how recurrent neural networks, statistical models and rule-based modules supply NLP building blocks today. They enable developers and data scientists without extensive natural language processing in artificial intelligence expertise to apply language AI techniques. As models grow more complex however, ease of use and interoperability warrant more focus within NLP tooling.
NLP in Customer Service
Natural language processing (NLP) allows businesses to understand and assist customers through written and spoken interactions. Chatbots natural language processing are becoming the first line of service while analyzing past conversations guides improvements.
LinkedIn Learning NLP
LinkedIn Learning provides online classes teaching beginner and advanced NLP concepts. These help customer service teams gain skills to develop NLP for AI applications like chatbots for common inquiries or tools to gauge sentiments across customer interactions. Courses also cover best practices in conversational interface designs and ethical considerations with using customer data.
Google AI NLP
Google’s natural language AI includes Dialogflow for creating conversational bots, Google Cloud NLP for text analysis and speech APIs for audio transcription. These assist customer service teams by automating repetitive requests, flagging priority issues and generating insights to better assist global users.
Alibaba NLP
Chinese retail giant Alibaba utilizes natural language processing across its platforms to deliver 24/7 self-service support. The AliMe chatbot handles millions of shopper interactions daily in Chinese. And NLP powers Alibaba’s customer data platform analyzing billions of touchpoints. This data-driven service approach resulted in over 70% resolution rates and 95% satisfaction scores last year.
Amazon NLP Model
Amazon Lex NLP models drive Alexa capabilities allowing hands-free support. Contact center AI tracks and resolves user issues faster using NLP on support calls. Additionally, Amazon Connect offers pre-built templates for building AI bots handling customer service scenarios like appointment scheduling and payment processing.
Facebook NLP Model
Facebook utilizes multilingual NLP to analyze user posts and detect issues impacting its global community. This allows swift redressal of complaints and unsafe content. For its family of apps focused on community interaction, NLP models understanding nuanced language are critical.
NLP Slack
Slack relies on NLP to optimize search across billions of professional conversations and surface relevant support content. Users can even query Slack in natural language to find answers from past discussions as needed without new tickets. Slack also uses NLP bots to assist with onboarding and task management.
NLP Netflix
Netflix taps NLP to break down patron conversations on social media and review sites to stay atop viewing interests and issues. Text analysis further enables personalized recommendations and targeted series promotions to drive growth. Behind the scenes, NLP chatbots support employees and streamline studio workflows.
As language AI progresses, virtually every customer interaction across industries promises to transform into helpful, productive conversations fueling mutual value.
Natural Language Generation in AI
Artificial intelligence can now generate very human-sounding text on demand for a wide variety of applications. New easy-to-use language models eliminate the need for any coding or deep learning skills.
No-code NLP
New no-code natural language processing tools allow anyone to access advanced AI text generation capabilities. Models like GPT-3 generate human quality language just from short text prompts without needing coding expertise.
GPT-3
GPT 3 NLP is a large language model from OpenAI trained on massive text datasets so it can continue writing text very naturally. The versatile GPT 3 natural language processing API enables many new applications for producing text content, having conversations, summarizing documents and more without any traditional programming.
Allen AI NLP
Groups like researchers at the Allen Institute for Artificial Intelligence are creating alternative natural language models with a focus on fairness, accuracy and factual integrity. Allen Institute NLP models intentionally avoid problematic biases that can plague AI systems.
Amelia NLP
Amelia is an conversational AI platform tailored for customer service needs. Developed by IPsoft, Amelia can understand questions and generate natural responses as a supportive automated agent or human-facing chatbot across service scenarios.
Chinchilla NLP
Chinchilla from startup Untapped AI specializes in text summarization. This NLP tool can digest documents across topics and condense their contents into concise overviews bringing out just the most essential salient points.
Clara NLP
Clara is a model designed specifically for safe and secure conversations. Created under Anthropic’s Constitutional AI approach to deliberately curb biases and toxicity, Clara is focused on being helpful, harmless and honest.
Cogito NLP
Cogito develops AI guidance software for call center employees focused on improving customer satisfaction. Their platform provides conversational guidance to human agents in real-time tailored to the specific dialogue needs of different industry clients.
Coursera NLP Deeplearning AI
Online education site Coursera now includes specialized machine learning course content on applying natural language processing techniques using deep learning and neural network methods as demand grows.
DaVinci NLP
DaVinci is OpenAI’s most advanced general text AI natural language generation model capable of highly advanced and nuanced language production rivaling human capabilities but with higher risks of problems like biased outputs.
DeepMind NLP
Researchers DeepMind, now owned by Alphabet, have focused on studying methods for making natural language models more secure as they grow more advanced to mitigate potential harms from misuse of generative AI technologies.
Deloitte NLP
As businesses hurriedly adopt the latest natural language models, Deloitte and other major management consulting firms now offer entire AI safety practices guiding responsible model implementation, auditing and change management around emerging generative AI tools.
Diffusion NLP
Diffusion is an image generation model capable of producing photos, digital art and more from natural language descriptions without any human involvement designed using a novel subclass of generative AI called diffusion models developed by startup Anthropic.
Grammarly NLP
Familiar writing enhancement tool Grammarly uses natural language processing to scan text passages checking grammar, spelling, stylistic issues and making enhancement recommendations targeted to a range of English language contexts.
Jarvis NLP
Jarvis is an AI assistant chatbot created by researchers at Anthropic focused on safe and secure conversational interactions. Built using Constitutional AI techniques that curb model harms, Jarvis targets being helpful, harmless, and honest.
Intelligent Chatbots with RASA, Dialogflow and Other NLP Toolkits
Intelligent conversational agents, also known as chatbots, are quickly transforming human-computer interactions across domains ranging from customer service to entertainment. Underneath these AI-driven bots, natural language processors analyze requests, formulate responses, and continually enhance their capabilities.
Several robust frameworks now exist to streamline building chatbots even without coding expertise. Tools like RASA and Dialogflow speed development using intuitive graphical interfaces instead of requiring complex programming.
Streamlined Chatbot Builders
RASA offers an open source set of NLP libraries focused specifically around building contextual chatbot assistants. Developers can leverage RASA’s built-in machine learning modules to parse language, recognize intents, manage conversations based on context, and generate relevant responses. The bot improves through ongoing training as it accumulates more real conversational data.
Developed by Google, Dialogflow also simplifies creating text or audio-based bots by providing prebuilt machine learning modules. Dialogflow integrates translation features supporting over 30 languages while handling speech recognition, rich messaging, and intelligent recommendations under the hood.
Both RASA and Dialogflow accelerate bot development by handling the heavy lifting around natural language processing. Designers can focus on high-level conversational logic and responses rather than data processing algorithms.
Customization and Integration Capabilities
While RASA and Dialogflow autogenerate basic chatbot functions, they allow substantial environment customization when needed. Sophisticated plugins, custom code integration, role-based account management, and secure cloud hosting enable tailoring bots to specific use case requirements.
For example, chatbots for medical appointments might connect to hospital databases to instantly retrieve patient records following personal health inquiries. Retail site bots could integrate product catalogs, inventory systems, and shipment tracking to locate items or delivery estimates for shoppers automatically.
These nimble toolkits aim to provide guards rails accelerating bot creation while offering escape hatches for advanced functionality when required via API integrations and plugin modules. Both open source and commercial versions are driving rapid enterprise adoption across sectors.
Carefully engineered chatbots promise to deliver immense value enhancing customer engagement, technical support, entertainment and more. But developers must remain vigilant around ethics, security and testing as these AI intermediaries permeate sensitive digital experiences.
Evaluating and Improving NLP Systems
Advances in natural language processing (NLP) have led to major progress in machine understanding and generating human language. As these AI systems expand into more products and services, priorities around safety, fairness and responsible development are growing.
Moving Forward Requires Careful Choices
As companies build more powerful NLP models, they increasingly deal with risks around potential biases, security problems, and lack of transparency. Groups like Microsoft, Google and IBM dedicate substantial efforts toward advancing AI ethics and ensuring customer trust.
Conversational AI tools from vendors like Salesforce use NLP to improve customer service chatbots. But human oversight remains essential to catch errors not identified during initial testing phases.
Addressing Biases and Explainability
Identifying and reducing biases has become a key focus area for improving NLP systems both internally and for external reviews. Techniques like Snorkel’s programmatic labeling also make the systems’ logic more understandable. Review boards and third-party audits are also becoming more widespread before deploying NLP chatbots and recommendation engines.
Advocacy groups also encourage tech leaders to increase accountability around risks from language AI – especially for high impact applications like content moderation.
Engineering for Security and Privacy
Engineering teams also prioritize building NLP services to safeguard sensitive customer information and prevent system misuse. Tools like Azure’s confidential computing enable predictions on encrypted data, insulating raw private inputs.
Going forward, scaling NLP responsibly requires collaboration between policy, ethics, and technology experts to guide development.
Future Progress Areas
In coming years, transfer learning shows promise for adapting models quickly between domains. Leaders like OpenAI and Cohere have shown models can transfer from one area (like computer code) to process natural language.
Feeding NLP systems multimodal data – combining text with visual, audio and sensor inputs – also unlocks new capabilities around contextual understanding and generation. Longer-term, assistants may seamlessly blend language with analysis of graphs, images and video.
Reinforcement learning can also optimize conversational styles and recommendation relevance by trying responses and refining strategies using real user feedback. As models ingest vast knowledge bases, they edge closer to human-level mastery of specialized fields.
Guiding these exponential gains with proactive policies and principles remains pivotal – allowing society to benefit from increasingly useful NLP applications while cultivating ethics at the core of engineering cultures. The priorities set today around responsible AI stand to shape outcomes for years.
Revenue from natural language processing (NLP) is expected to grow very quickly in the next few years. NLP is a type of artificial intelligence that helps computers understand and work with human language.
In 2017, the NLP market was around 3 billion dollars. By 2025, it is forecast to grow to over 43 billion dollars – almost 14 times bigger.
NLP combines ideas from computer science and linguistics to try to close the gap between human communication and computer understanding. It has many uses in areas like search engines, chatbots, translation tools, and more.
As NLP expands into more technologies, worldwide artificial intelligence revenue overall is also predicted to grow rapidly. Total worldwide AI software revenue could reach around 126 billion dollars by 2025.
Regions like North America and Asia are likely to see the biggest AI revenue increases. And AI is expected to make up over 10% of total economic production in these regions by 2030. China may see the largest GDP growth from AI – potentially around 26%.
So NLP specifically and AI overall are expected to continue expanding significantly in the next decade. This fast progress reflects all the new ways AI like NLP can automate tasks, improve products, and enhance productivity.
