Artificial Intelligence What is natural language processing (NLP) & how it works?

What is Natural language processing? The question baffles every beginner AI enthusiast. The more you know about Natural Language Processing applications, the more it leaves you astonished. Here is all you need to know about NLP and its applications.

 Natural language processing (NLP) is the ability of a computer to understand human speech. It is a subfield of artificial intelligence and machine learning that involves analyzing, understanding, and generating natural language. NLP-based applications are used in many areas, including information retrieval, text mining, question answering systems, speech recognition and production, machine translation, social network filtering and analysis.

Importance of natural language processing in AI

NLP allows machines to understand human language. It bridges the human-to-machine gap by using Artificial Intelligence (AI) to process text and the spoken word. It’s a form of AI that manipulates and interprets human language using computational linguistics (the parsing out of parts of speech and words into data). NLP consists of two branches. Natural Language Understanding (NLU) extracts the meanings of words by studying the relationships between them. And Natural Language Generation (NLG) transforms data into understandable language, writing sentences and even paragraphs that are appropriate, well-constructed, and often personalized. It allows computers to create responses for chatbots and virtual assistants, write subject lines for emails, and even compose advertising copy and marketing tools.

Breaking it down

Here’s how to think about it: NLU focuses on your computer’s ability to read and listen (speech-to-text processing), and NLG allows it to write and speak (text-to-speech). Both are part of NLP. And Natural Language Processing applications are everywhere. Intelligent Personal Assistants (IPAs) answer customer questions. Voice assistants like Siri respond to commands. Marketers use it to create custom content, personalize or push specific promotions, and personalize offerings to the taste of a particular shopper. Auto-complete and auto-correct functions in texting either help you or drive you nuts. Machine translation tools clue you into words from other languages. Even brick-and-mortar stores can take advantage by customizing individual stores’ landing pages to show local hours of operation, addresses, directions, and additional information.

Main Difference

NLP is closely related to computational linguistics and computational semantics. The main difference between the two fields is that NLP focuses on developing models that can be implemented using computers while computational linguistics focuses on theoretical aspects of modeling languages with computers.

The term “natural language processing” was coined by Michael Brady in 1959 for his work at IBM on an application called “A computer program for American English grammar checker” which was published in 1960. In 1965 he also published a paper titled “A Program to translate poetry from Chinese into English” which was one of the first works where machine translation was applied to practical problems. The field has since grown beyond recognition with many new applications being developed every year such as Siri or Google Translate.

How does natural language processing work?

Natural language processing is a very broad field and there are many different approaches to solving the same problem. The most common approach is to use machine learning algorithms that can be trained on large amounts of data. There are also some more specific approaches such as using logic programming or symbolic reasoning. In this article we will focus on the most common approach which uses machine learning algorithms.

The first step in NLP is usually to break down the text into smaller pieces called tokens. Tokens can be words, numbers, punctuation marks etc. After breaking down the text into tokens it’s possible to do some basic analysis such as counting how many times each word appears in the text or finding out what part of speech each token belongs to (noun, verb etc.). This process is called tokenization and it’s usually done by regular expressions which are simple patterns used for matching characters or groups of characters in a string of text.

Example

For example, here’s a regular expression that matches all digits: [0-9] . & another one that matches all lowercase letters: [a-z] . Regular expressions are very powerful but they can also be hard to read and write so there are libraries like nltk that provide simpler ways for writing them.

After tokenization comes part-of-speech tagging where each token is labeled with its part of speech (noun, verb etc.). Part-of-speech tagging was one of the first tasks in NLP and it’s still one of the most important ones.

There are many different approaches to part-of-speech tagging but they all work by using machine learning algorithms that have been trained on a large corpus of text. The most common approach is called maximum entropy which uses a statistical model for predicting the probability that each word belongs to a certain part of speech.

After part-of-speech tagging comes parsing where sentences are broken down into smaller pieces called phrases or clauses. This process is usually done by using recursive descent parsers which are very similar to regular expressions except that they can be applied recursively over an input string instead of matching characters one at a time. For example, here’s how you would write a regular expression for matching English sentences:

S -> NP VP | VP PP | PP S -> NP PP | VP PP -> P NP -> Det Noun Verb Adj | Noun Verb Adj -> Det Noun Verb Adj | Noun Verb Adj Det -> [the] [a] [my] [your] [his] etc.

Here’s how you would write the same thing as a recursive descent parser:

def parse_sentence(text): if text == ”: return [] elif text[0].isalpha(): return parse_noun_phrase(text) + parse_verb_phrase(text) else: return parse_verb_phrase(text) + parse_noun_phrase(text) def parse_noun_phrase(text): if text[0].isalpha(): return [parse_noun(text)] else: return [parse_verb(text)] def parse_verb_phrase(text): if text[0].isalpha(): return [parse_verb(text)] else: return [parse] def parse_noun(text): if text[0].isdigit(): return [int.strip()] elif text[0].islower(): return [lower.strip()] else: return [‘det’ + nltk.pos_tag(text[0]) for n in nltk.pos_tagging(‘the’, ‘a’, ‘my’, ‘your’, ‘his’)] def parse_verb(text): if text[0].isdigit(): return [‘v’ + int.strip()] elif text[0].islower(): return [‘v’ + lower.strip()] else: raise Exception(“Invalid verb”)

The last step is to do a kind of analysis on the parsed sentences such as finding out which words are most important or which parts of speech are used most often etc. This process is called semantic analysis and it’s usually done by using statistical models that have been trained on a large corpus of annotated data (data where each token has been labelled with its part of speech). The most common approach is called latent semantic analysis which uses a statistical model for predicting the probability that each word belongs to a certain part of speech

NLP Benefits: Transforming the Digital Landscape

Imagine a world where computers understand us as effortlessly as our closest confidants. Natural Language Processing (NLP) is the groundbreaking technology that brings us closer to that reality. By enabling machines to comprehend and process human language, NLP opens up a realm of possibilities and benefits that revolutionize our interactions with technology. Let’s delve into the captivating advantages of NLP and discover how it’s reshaping our world.

Enhanced Communication: 

Breaking down the language barrier, NLP bridges the gap between humans and machines, fostering seamless communication. From virtual assistants that grasp our intents to chatbots engaging in witty banter, NLP creates more meaningful and natural interactions, making technology feel like a trusted companion.

The Power of Understanding: 

NLP empowers us to extract crucial information from vast troves of unstructured text data. Think of it as a detective that unravels the hidden insights within social media posts, customer reviews, and news articles. By deciphering sentiments, identifying topics, and recognizing entities, NLP equips businesses with a wealth of knowledge, enabling data-driven decision-making.

Analytical Prowess: 

Say goodbye to the daunting task of manually analyzing mountains of text. NLP automates the labor-intensive process, empowering businesses to swiftly categorize, organize, and extract valuable insights from textual data. Sentiment analysis, topic modeling, and entity recognition become effortless, unlocking a treasure trove of information with just a few clicks.

Elevating Customer Experience: 

Ever had a conversation with a chatbot that left you impressed? Chances are it was powered by NLP. By understanding customer inquiries and providing relevant responses, NLP-driven chatbots and virtual agents deliver exceptional customer experiences. With lightning-fast response times and personalized interactions, businesses can forge deeper connections and foster customer loyalty.

Time and Cost Magic: 

NLP wields the power to tame the time-consuming beasts lurking in our workflows. It effortlessly summarizes lengthy documents, translates languages, and retrieves information in a fraction of the time it would take a human. By automating these processes, NLP gifts us precious hours and cost savings, allowing us to focus on what truly matters.

Embracing Multilingualism: 

Language knows no boundaries, and neither does NLP. It gracefully dances across languages, embracing the diversity of our global village. Whether it’s breaking language barriers through translations, deciphering sentiments in multiple tongues, or facilitating cross-lingual information retrieval, NLP fosters understanding and connection in an interconnected world.

Illuminating Insights: 

NLP doesn’t just analyze words; it uncovers the beating heart of human sentiments and opinions. By deciphering textual data, NLP provides businesses with a deep understanding of their customers’ preferences, market trends, and brand perception. Armed with these insights, businesses can craft strategies that resonate with their target audience and stay ahead of the competition.

The Decision-Making Revolution: 

NLP becomes the trusted advisor in the decision-making process, arming us with accurate and relevant information. By gauging public sentiment, tracking emerging trends, and extracting critical details from documents, NLP empowers businesses to make informed choices. Say goodbye to guesswork and hello to data-driven success.

Automation Euphoria: 

Mundane tasks stifling productivity? NLP swoops in to save the day. It automates repetitive endeavors like data entry, content generation, and customer support, freeing up valuable time and resources. Embracing NLP’s automation superpowers, businesses can soar to new heights of efficiency and devote their energy to innovation.

Inclusivity Unveiled: 

NLP goes beyond mere words; it’s a catalyst for inclusivity and accessibility. By offering translation services, speech recognition, and text-to-speech capabilities, NLP ensures that language barriers and accessibility needs are shattered. Everyone deserves to be heard and understood, and NLP paves the way for a more inclusive digital landscape.

NLP Streamlined Communication for ALL

In a world where communication is king, NLP emerges as the crown jewel, revolutionizing how we interact with technology. With its ability to understand, analyze, and automate language-related tasks, NLP ignites a transformative spark across industries. So, embrace the power of NLP and embark on a journey where machines truly comprehend our words, intentions, and desires. The future of communication has arrived, and it’s awe-inspiring.

Here is How Google uses NLP – The Case of BERT

Bidirectional Encoder Representations from Transformers or BERT is an NLP derived artificial language model. BERT is currently being used by top businesses like Google, AWS, IBM, Microsoft and Baidu across a range of applications. Before BERT, Google relied on other language models for understanding the human language inputs at various fronts. However, BERT has a better application as it allows Google to reach beyond the search and know more about the context of the human language.

BERT takes into account all surrounding words of a target word instead of just putting all the focus on the words immediately before or after the target word. Therefore, BERT carefully finds the context of every word. BERT enhances user-friendliness and allows Google to better understand and respond to user queries. Since BERT is fed with over 2.5 billion words and has the capability to understand new words, it massively helps Google to offer relevant search results for misspelled or poorly worded search queries.

BERT recognizes “keywordese”

BERT can break down complex syntax queries and figure out the request semantics. It helps Google users, who are attempting to speak computer, to get better results based on keywordese. On top of that, BERT can also account for a word’s context – indicating that it can distinguish whether a specific word is used as noun, a verb or an adjective. Google uses BERT to optimize its search accuracy and extend its reach into summarizations and chatbots.

There are many different applications for natural language processing and they can be divided into three main categories: information retrieval, text mining and question answering systems. Information retrieval is used in search engines where the goal is to find documents that match a given query. Text mining is used in document classification where the goal is to assign labels to documents based on their content. Question answering systems are used in chatbots and virtual assistants where the goal is to answer questions from users as accurately as possible. In this article we will focus on information retrieval and text mining but there will also be some examples of question answering systems at the end.

GPT-3 Revolution in Natural Language Processing Conversational AI

The recent advancements in the machine learning regimen has led to the development of GPT-3 language transformers. GPT-3 is preferred transformer as it can:

• Utilize online/internet data to generate text
• Take a chunk of input text and generate huge volume of sophisticated AI generated text
• Analyze the input of language and predict about the context of writer or speaker 

GPT-3 is gaining popularity in content generators as it is quite useful in composing short form as well as long form content. This transformer can not only compose tweets and press releases but also long blog posts and computer code. GPT-3 is based on Natural Language Generation principles. Thus, it can create easy-to-understand summaries and responses. Moreover, it can also generate contextual keywords automatically based on input. GPT-3 has opened new avenues of deep learning in machine learning, making it very easy to generate social media copies and content everyday. 

Learn More about the latest NLP Models.

What is Natural Language Processing’s Role in Content Marketing?

Natural Language Processing conversational AI offers tons of exciting applications for content marketers. Once successfully applied, natural language processing becomes a vital component of your content marketing strategy.

• Analyzing and considering content sentiment 
• Determining the most accurate and relevant  keywords
• Writing long form blog posts and sales product descriptions for ecommerce stores
• Helping to reinforce content marketing strategy based on content audits and client profiling
• Refining chatbot functions to improve lead generation and engagement
• Generating and Scaling content to align with content marketing plans

What are Natural Language Processing Applications?

Information retrieval (IR)

Information retrieval (IR) refers to all kinds of techniques for finding documents that match a given query or set of queries. The most common application of IR is search engines which have become an essential part of our everyday lives with services like Google, Bing, Yahoo etc. There are many different approaches for building search engines but they all work by using machine learning algorithms that have been trained on large amounts of data such as web pages or news articles etc. The most common approach is called latent semantic analysis which uses a statistical model for predicting the probability that each word belongs to a certain part of speech (noun, verb etc.). This approach has been very successful but it’s also very hard because it requires training models in huge amounts.

Text mining (TM)

Text mining refers to all kinds of techniques for extracting information from documents. The most common application of text mining is document classification where the goal is to assign labels to documents based on their content. For example, you could use text mining to automatically label news articles as either positive or negative. There are many different approaches for building document classifiers but they all work by using machine learning algorithms that have been trained on large amounts of data such as web pages or news articles etc. The most common approach is called latent semantic analysis which uses a statistical model for predicting the probability that each word belongs to a certain part of speech (noun, verb etc.). This approach has been very successful but it equires huge amounts of data  training models.

Question answering systems (QA)

Question answering systems are used in chatbots and virtual assistants where the goal is to answer questions from users as accurately as possible. In this article we will focus on question answering systems but there will also be some examples of information retrieval and text mining at the end. There are many different approaches for building question answering systems but they all work by using natural language processing machine learning algorithms that have been trained on large amounts of data such as Wikipedia or news articles etc. The most common approach is called latent semantic analysis which uses a statistical model for predicting the probability that each word belongs to a certain part of speech (noun, verb etc.). 

Speech recognition and production (SRP)

Speech recognition and production are the two main components of natural language processing (NLP). It is the process of converting speech into text. The speech production is the reverse process, converting text into speech.

The speech recognition system is based on the Carnegie Mellon University Sphinx project. The system uses a Hidden Markov Model (HMM) to model the acoustic and language models of the speech signal. The HMM is trained using a large corpus of recorded speech, which is then used to recognize new utterances.

The speech production system uses an articulatory synthesis model that has been trained using data from many speakers. This model can be used to synthesize new utterances in a variety of languages, including English, French, German and Spanish.

Machine Translation (MT)

Machine translation is a technology that allows you to translate text from one language into another. It is used in NLP to translate the contents of documents and websites into English.

How does machine translation work?

Machine translation works by using computer software to analyze the structure of words and sentences in a document or website, and then convert them into an equivalent text in another language. The process involves breaking down the original text into its component parts (words, phrases, etc.) and then reassembling them in a different order. This means that machine translations are not always accurate or natural-sounding. They can also contain errors due to differences between languages (for example, word order). Machine translations are therefore best used as a starting point for human translators who can correct any mistakes before they become part of the final product.

Social network filtering and analysis (SNFA)

Social network filtering and analysis is a process of identifying the people who are most influential in a social network. This can be done by looking at the connections between individuals, or by looking at the content that they share.

Social network filtering and analysis can be used to identify:

• Who is most influential in a social network?
• What content is most popular in a social network?
• How can I use social network filtering and analysis to identify the people who are most influential in a social network?

You can use NLP to identify the people who are most influential in a social network by looking at their connections. This is done by using the ‘People’ tab on the left hand side of your screen. You can then click on ‘Connections’, which will show you all of the connections between individuals. The more connections an individual has, the more likely they are to be influential within that community.

You can also look at how many followers an individual has, or how many times they have been retweeted or liked. These metrics give you an indication of how much influence someone has within that community. For example, if someone has lots of followers and gets retweeted often, it suggests that they have high levels of influence within that community.

NLP and WriteMeAI

NLP (Natural Language Processing) is integral to AI writing tools like WriteMe AI Writer. It enables the tool to understand and interpret human language, generate fluent and contextually relevant text, perform language translation, analyze sentiment, check grammar and spelling, summarize text, and recognize user intent. NLP enhances the capabilities of WriteMe AI Writer, making it a powerful tool for generating high-quality written content efficiently and accurately.

NLP helps WriteMeAI in:

• Understanding Human Language
• Fluent and Relevant Text Generation
• Accurate Language Translation
• Sentiment Analysis
• Grammar and Spell Checking
• Text Summarization
• Intent Recognition 

Emerging Trends in Natural Language Processing Market

In the exciting field of Natural Language Processing (NLP), there are several latest trends that are shaping the way we interact with language and data. Let’s explore some of these trends and gain insights into their natural language processing tools:

Virtual Assistants: 

Virtual assistants have gained immense popularity, and startups are leveraging NLP to develop novel virtual assistants and chatbots. These assistants improve accessibility, provide on-demand information, and find applications in various domains. For example, Servicely develops Sofi, an AI-powered self-service automation software that enhances service desk operations, and Vox automates conversational experiences to personalize customer interactions.

Sentiment Analysis: 

Startups are focusing on NLP models that understand the emotional or sentimental aspect of text data, leading to improved customer loyalty and better service delivery. Y Meadows automates customer support requests using AI, while Spiky delivers video sentiment analytics to enhance sales calls and coaching sessions.

Multilingual Language Models: 

With the aim of improving data accessibility and increasing brand reach, NLP models that accurately understand unstructured data in different languages are gaining attention. Lingoes offers a no-code multilingual text analytics solution, enabling marketing teams, product teams, and developers to monitor sentiments, analyze feedback, and create multilingual NLP classifiers. NLP Cloud provides pre-trained multilingual AI models for text understanding and generation, catering to various industries.

Named Entity Recognition (NER): 

NER models are revolutionizing data extraction workflows across industries. They classify and annotate data, including person names, organizations, dates and times, email addresses, and more. M47AI offers AI-based data annotation to improve data labeling, while HyperGlue develops an analytics solution for generating insights from unstructured text data.

Language Transformers: 

Language transformers employ self-attention mechanisms to better understand the relationships between sequential elements, addressing challenges like similar-sounding words and improving the performance of NLP models. Build & Code aids construction document processing using language transformers and a proprietary knowledge graph, while Birch.AI automates call center operations by applying transformer-based NLP models for understanding complex conversations.

Transfer Learning: 

Transfer learning plays a crucial role in optimizing the general process of deep learning by sharing training data among language models. It reduces the time and cost required to train new NLP models. Got It AI creates autonomous conversational AI using transfer learning and NLP models, and QuillBot offers an AI-powered paraphrasing tool using transfer learning and natural language generation.

Text Summarization: 

Startups are developing NLP models that summarize lengthy texts into cohesive and fluent summaries, saving time and increasing productivity. SummarizeBot utilizes blockchain technology to provide real-time information summaries, while Zeon AI Labs offers an intelligent search platform, DeepDelve, that provides accurate and contextual answers to questions based on enterprise documents.

Semantic Search: 

Semantic search models, enabled by NLP, analyze search intent and provide more relevant results. deepset offers a cloud-based NLP platform, Haystack, for building natural language interfaces and scalable semantic search systems. Vectra provides a neural search-as-a-service platform, Vectra Neural Rank, that gains a deeper understanding of questions without the need for retraining or tuning.

Reinforcement Learning: 

Reinforcement learning enables NLP models to continuously improve their performance through reward-based training iterations, leading to enhanced applications in healthcare, translation software, chatbots, and more. AyGLOO creates an explainable AI solution using reinforcement learning to optimize NLP techniques, while VeracityAI specializes in natural language model training with a reinforcement learning-based recommender system.

Suggested Read: How Can AI Help With Content Marketing?

Start Using Natural Language Processing Applications

Natural language processing is a powerful tool which can support a myriad of natural language processing applications. Thus, it can boost your content marketing efforts. AI Content Generators using natural language processing will transform your content marketing efforts, delivering better results on every front.