Complete Guide to Natural Language Processing NLP with Practical Examples

An Introduction to Natural Language Processing NLP

You can then be notified of any issues they are facing and deal with them as quickly they crop up. These smart assistants, such as Siri or Alexa, use voice recognition to understand our everyday queries, they then use natural language generation (a subfield of NLP) to answer these queries. Natural language processing is developing at a rapid pace and its applications are evolving every day. That’s great news for businesses since NLP can have a dramatic effect on how you run your day-to-day operations. It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers. Through NLP, computers don’t just understand meaning, they also understand sentiment and intent.

• In summary, a bag of words is a collection of words that represent a sentence along with the word count where the order of occurrences is not relevant.
• Topic Modeling is an unsupervised Natural Language Processing technique that utilizes artificial intelligence programs to tag and group text clusters that share common topics.
• Search engines leverage NLP to suggest relevant results based on previous search history behavior and user intent.
• NLP can be used for a wide variety of applications but it’s far from perfect.

This experimentation could lead to continuous improvement in language understanding and generation, bringing us closer to achieving artificial general intelligence (AGI). This powerful NLP-powered technology makes it easier to monitor and manage your brand’s reputation and get an overall idea of how your customers view you, helping you to improve your products or services over time. NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors. The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check.

How Does Natural Language Processing Work?

Human language has several features like sarcasm, metaphors, variations in sentence structure, plus grammar and usage exceptions that take humans years to learn. Programmers use machine learning methods to teach NLP applications to recognize and accurately understand these features from the start. Since stemmers use algorithmics example of nlp approaches, the result of the stemming process may not be an actual word or even change the word (and sentence) meaning. Always look at the whole picture and test your model’s performance. NLP is broadly defined as the automatic manipulation of natural language, either in speech or text form, by software.

Analytically speaking, punctuation marks are not that important for natural language processing. Therefore, in the next step, we will be removing such punctuation marks. Natural Language Processing started in 1950 When Alan Mathison Turing published an article in the name Computing Machinery and Intelligence. It talks about automatic interpretation and generation of natural language. As the technology evolved, different approaches have come to deal with NLP tasks.

Language Translation

Natural language processing can help customers book tickets, track orders and even recommend similar products on e-commerce websites. Teams can also use data on customer purchases to inform what types of products to stock up on and when to replenish inventories. The letters directly above the single words show the parts of speech for each word (noun, verb and determiner). One level higher is some hierarchical grouping of words into phrases.

Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data. Request your free demo today to see how you can streamline your business with natural language processing and MonkeyLearn. Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products.

Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar. Grammatical rules are applied to categories and groups of words, not individual words. The effective classification of customer sentiments about products and services of a brand could help companies in modifying their marketing strategies. For example, businesses can recognize bad sentiment about their brand and implement countermeasures before the issue spreads out of control. The next entry among popular NLP examples draws attention towards chatbots. As a matter of fact, chatbots had already made their mark before the arrival of smart assistants such as Siri and Alexa.

Many languages don’t allow for straight translation and have different orders for sentence structure, which translation services used to overlook. With NLP, online translators can translate languages more accurately and present grammatically-correct results. This is infinitely helpful when trying to communicate with someone in another language. Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it.

Statistical NLP (1990s–2010s)

In the following example, we will extract a noun phrase from the text. Before extracting it, we need to define what kind of noun phrase we are looking for, or in other words, we have to set the grammar for a noun phrase. In this case, we define a noun phrase by an optional determiner followed by adjectives and nouns. Notice that we can also visualize the text with the .draw( ) function.

Some services provide an all in one solution while some focus on resolving one single issue. Artificial intelligence is all set to bring desired changes in the business-consumer relationship scene. Chatbots primarily employ the concept of Natural Language Processing in two stages to get to the core of a user’s query. This ensures that users stay tuned into the conversation, that their queries are addressed effectively by the virtual assistant, and that they move on to the next stage of the marketing funnel.

Implementing NLP Tasks

In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code. From overseeing the design of enterprise applications to solving problems at the implementation level, he is the go-to person for all things software. There are many NLP engines available in the market right from Google’s Dialogflow (previously known as API.ai), Wit.ai, Watson Conversation Service, Lex and more.

However, what makes it different is that it finds the dictionary word instead of truncating the original word. That is why it generates results faster, but it is less accurate than lemmatization. In the code snippet below, we show that all the words truncate to their stem words. As we mentioned before, we can use any shape or image to form a word cloud. Notice that we still have many words that are not very useful in the analysis of our text file sample, such as “and,” “but,” “so,” and others. Next, we can see the entire text of our data is represented as words and also notice that the total number of words here is 144.

We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. Additionally, while all the sentimental analytics are in place, NLP cannot deal with sarcasm, humour, or irony. Jargon also poses a big problem to NLP – seeing how people from different industries tend to use very different vocabulary. Unless the speech designed for it is convincing enough to actually retain the user in a conversation, the chatbot will have no value.

This can save time and effort in tasks like research, news aggregation, and document management. Sentiment analysis determines the sentiment or emotion expressed in a text, such as positive, negative, or neutral. While our example sentence doesn’t express a clear sentiment, this technique is widely used for brand monitoring, product reviews, and social media analysis. Lemmatization, similar to stemming, considers the context and morphological structure of a word to determine its base form, or lemma. It provides more accurate results than stemming, as it accounts for language irregularities. Levity is a tool that allows you to train AI models on images, documents, and text data.

This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised. Smarter versions of chatbots are able to connect with older APIs in a business’s work environment and extract relevant information for its own use. They can also perform actions on the behalf of other, older systems. As you can see in the example below, NER is similar to sentiment analysis.

You can see it has review which is our text data , and sentiment which is the classification label. You need to build a model trained on movie_data ,which can classify any new review as positive or negative. Spacy gives you the option to check a token’s Part-of-speech through token.pos_ method. Hence, frequency analysis of token is an important method in text processing. The stop words like ‘it’,’was’,’that’,’to’…, so on do not give us much information, especially for models that look at what words are present and how many times they are repeated.

Every time you type a text on your smartphone, you see NLP in action. You often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more you text, the more accurate it becomes, often recognizing commonly used words and names faster than you can type them.