Natural Language Processing: A Guide To NLP Usecases, Approaches and Tools

 

Since the beginning of time, human beings have attempted to make machines talk. Alan Turing saw computer generation natural speech as evidence of computer thought. Nowadays, virtual companions are available to us all. Despite years of research, innovation, and natural responses, these virtual companions remind us that, no, we aren't yet at HAL-9000-level speech sophistication.

Machines are able to make sense of the language and speech we use in other situations, even though they might not understand us at times. Internet search engines can be extremely useful in answering our queries. Language translation is faster and more accurate than ever. Advanced grammar checks also save our reputation when we send emails. Natural Language Processing technology is also available for all businesses.

Keep reading for more information:

NLP can solve these problems

There are many options available for text processing, and it is up to you which one you prefer.

Particulars about the data used for NLP

You can use these tools to create NLP models

Finally, let's talk about what blocks NLP adoption. And how to get around it.

What is Natural Language Processing (NLP)? Principal NLP Use Cases

Natural language processing (or NLP) is a branch in Artificial Intelligence that gives computers the ability to understand natural speech. Computers are able to understand context, sentiment, and words using linguistics, statistics and machine learning.

Text classification

In daily life as well business, we deal a lot with unstructured text data. Emails, legal documents and product reviews are just a few examples. They come in many formats which make it difficult to store and retrieve. It is this reason that many companies do not realize the potential value it can bring to their business.

Text classification is one fundamental technique of NLP that organizes and categorizes text to make it easier to understand. For example, you could assign tasks according to urgency or distinguish between negative and positive comments among all your feedback.

These are some of the most common uses for text classification.

Sentiment analysis. This classification of text is based on the author's emotions, judgments, or opinions. Brands can use sentiment analysis for customer service to identify trends in the industry, prioritize customer service tasks and learn about their customers.

Spam detection. ML-based spam detector technologies can filter spam emails out of genuine ones with very few errors. These systems detect slight signs of spam mail, including bad grammar and spelling, urgency and financial language.

Language detection. Language detection is used to help customers find the right team by highlighting the language used in chats and emails. This can also be useful in fraud and spam detection since language is often changed to hide suspicious activity.

Information extraction

Information extraction (IE), which uses NLP to extract unstructured text data, is another method. Information extraction (IE), which is a way to obtain predefined information like a person's name and a date of the event, a number, etc. and organize it into a database, allows you to do this.

Information extraction is a key technology used in many high-level tasks such as:

Machine translation. Google Translate, a translation tool that uses NLP, does not just replace words with words in another language but provides contextual meaning. It also captures the tone and intention of the original text.

Intelligent document processing. Intelligent Document Processing extracts data automatically from various documents and then converts it to the desired format. It makes use of NLP and computer visual to extract valuable information from a document and classify it.

Answering questions. Siri, Alexa, and ML-based chatbots are virtual assistants that pull information from unstructured sources and other image data collection of chats to answer questions in natural language. These dialog systems are difficult to create and remain a problem in NLP. This is a good thing because there is a lot of research.

Language modeling

GPT-3, a state-of the-art language model capable of producing eerily natural text, might be something you've heard. The model predicts the next sentence word based on all the words before it. Because it has been trained with hundreds of billions upon billions of samples, not all language models are as powerful as this one. The same principle of calculating probabilities of word sequences can also be used to create language model that mimics human speech.

Speech recognition. Machines recognize spoken text by creating its phonetic maps and then determining what combinations of words will fit the model. The machine analyzes the entire context and uses language modeling to figure out what word should go next. This technology is behind virtual assistants and subtitle creation tools.

Text summarization. Extraction method is also a way to reduce the complexity of the text down to a handful of key informational elements. To create an abstract that will generate the summary, you will need sequence to sequence modelling. This is useful for creating automated reports, generating a news feed, annotation texts, and much more. GPT-3 does the same thing.

While this list isn't exhaustive, it gives a general overview of NLP's many applications. Let's get to the core methods of NLP and when they should be used.

Approaches to NLP - Rules vs traditional ML vs neural network

NLP techniques provide endless possibilities for human-machine interactions, which we've explored for decades. Since the 70s script-based systems have been able to fool people into thinking that they are speaking with a human. Machine learning and deep-learning algorithms allow programs to go beyond just picking the right line of reply. They can also help with speech and text processing issues. Each of these techniques can still be used together, and each makes sense for certain situations. Let's take a look at each one.

Rule-based NLP -- ideal for data preprocessing

Rules are an outdated way to process text. They are manually written and can be used to automate routine tasks. Although you may be able to write rules that allow the system recognize an email address within the text, it is not possible to create any other types of rules. Once any new variety is introduced, however, the system's capabilities are limited.

Rules are still being used today, however, because they can be effective in certain circumstances. This includes those tasks that are:

A current rule base. An existing rule base. For example, grammar already has a set of rules. While a dictionary is a great tool, it won't be in a position to suggest better words or phrases.

Specifications for domains. Rule writers need to have a good grasp of the domain. Even grammar rules must be adapted to fit the system. A linguist is the only person who knows all the details.

Small sets of rules. You will soon have hundreds of rules and your maintenance costs will be prohibitive.

Rules are used frequently in text preprocessing required for ML based NLP. Tokenization (splitting text into words) or part-of-speech tag (labeling nouns and verbs) are just two examples. Rules can be used to accomplish these tasks.

NLP using machine learning -- the basic method for NLP

Machine learning (also known statistical) is a method of NLP that uses AI algorithms to solve problems, without having to be programmed. Instead of working with patterns written by humans, ML models are able to find patterns in text simply by analyzing it. These will not be the same texts that we see. The two main steps to prepare data for the computer to understand are:

Annotation and formatting of text. Preparation of data is the foundation of any ML project. You can watch our video to find out more about data preparation.

This process is known as building a corpus in NLP tasks. Corpora are plural for corpus. They are collections or texts that are used to train ML. You cannot just give the system a whole set of emails and expect it will understand your requests. Annotating texts is a way to enhance their meaning. Some forms of annotations include parsing, part-of speech tagging and tokenization. Structure is achieved when annotated data are organized in standard formats.

Feature engineering. Machines can perceive text using features, which are different from a corpus. The features are characteristics such as language, word count, punctuation count or word frequency that tell the system what text is most important. Data scientists determine what text data collection features will help solve the problem by using their domain knowledge and creative skills. For example, the frequency feature of the words now, instantly, free and call will show that the message has been marked as spam. The punctuation counts feature will allow you to use exclamation points.

Modell training and deployment. The algorithm is then trained with the data. Supervised learning, which is training with labeled data, is known as supervised learning. This method works well for most types of classification problems. Decision Trees are some of the most popular NLP algorithms. Data scientists validate the model and verify it after it has been trained. AI developers sometimes use pretrained language models designed to solve specific problems. In the following sections, we will discuss this.

Just as rule-based approaches require linguistic knowledge to create them, machine learning methods can only be as good as the data quality and the accuracy of features that are created by data scientists. This means that ML is not as good at classification as rules. However, it does a poor job in two other areas.

Complexity of feature engineering means that researchers must do extensive preparations before they can automate ML.

The curse of dimensionality, where the data volumes required grow exponentially with the model's dimensions, resulting in data sparsity.

This is why a lot research in NLP focuses on deep learning, a more sophisticated ML approach.

NLP-based deep learning -- state-of the-art, trendy methods

Deep learning is also called deep neural networks. This branch of machine-learning simulates how human brains function. It is deep because of the many interconnected layers it contains. The input layers (or synapses as it is known in biological analogies), receive data from each other and then transmit it to hidden layers, which carry out complex mathematical computations.

Because neural networks are so powerful, they can receive raw data (words that are represented as vectors), without the need for any pre-engineered features. Networks can learn from each other what is important.

NLP has been elevated to a whole new level thanks to deep learning. Two breakthrough achievements made it possible.

Word embeddings We represent data numerically when we feed it to machines. That's because computers can understand data. This representation must not only contain the word's meaning but also its context, and any semantic connections to other terms. Vectors, also known as word embeddings, are used to pack all this data in one representation. Models are better able to predict and make predictions because they can capture relationships between words.

Attention Mechanism. Human cognition inspired this technique to enhance the most important parts a sentence and to allocate more computing power. The original purpose of the attention mechanism was to facilitate machine translation tasks. The encoder takes in the input sentence and converts it to an abstract vector. The decoder transforms the vector into a sentence in a target languages. The system used the attention mechanism to distinguish the most significant parts of the sentence, and then allocated most of the computing power to them. This allowed data scientists the ability to handle lengthy input sentences.

The attention mechanism revolutionized deep learning models. Now it is used for much more than translation tasks. It handles speech input from humans, which allows voice assistants such as Alexa and others to accurately recognize speaker intent.

Deep learning is an advanced technology that can be used for many NLP tasks. But real-life applications combine all three methods, by improving neural networks with rules or ML mechanisms. These methods can be expensive.

These calculations require huge computational resources.

Training neural networks requires massive amounts of data.

With a better understanding of the principles and methodologies behind building NLP models let's get to the core of all ML projects: the dataset. What is the best way to prepare one?

How to prepare an NLP data set

NLP success depends on having great data. But what makes data exceptional? Data is essential for ML, and even more so for deep learning. However, it is important to make sure the quality of the data doesn't suffer just because the volume was prioritized. The following are the most important questions ML researchers should answer when preparing data.

How can we be sure that we have enough information to produce useful results?

How do we determine the quality of our data?

These considerations apply to both private and public data. Let's discuss both of these issues.

Determining dataset size

For an accurate outcome, no one can tell how many product reviews and emails, sentence pairs, questions/answers, or sentence pairs you'll need. To illustrate, 100,000 hotel reviews were collected from public resources for our sentiment analysis tool. However, there are ways to determine how large a dataset is suitable for your project. These methods were suggested by Jason Brownlee (ML specialist).

Follow an example. NLP projects are ongoing and people publish their results in blogs and papers. To get an estimate, look for similar solutions.

Do your research. To accurately identify the amount of data required to capture the task's complexity, either consult domain experts or your own knowledge.

Use statistical heuristics. There are several statistical techniques that can identify sample size for any type of research. Consider the number and percentage of features (x% less examples than number), model parameters (x examples each parameter), or the number classes.

Guesstimate/get as far as you possibly can. These are popular but unreliable methods that will help you get started. Additionally, it is unlikely that you will be able to use enough data.

Quality assessment of text data

There are many opinions on what is considered high-quality data in various areas of application. Representational is a quality parameter that is very important in NLP.

Representational quality indicators measure how easy it for machines to understand the text. These problems are found in the data:

Incorrectly formulated data values (same entities, different syntax)

Correct spelling and typing errors

Different spellings of a single word

Problems with co-reference (the same person mentioned in the text can also be called Oliver, Mr. Twist, Twist the boy, he etc. );

Lexical ambiguity refers to words or phrases that are used in different contexts and can have different meanings. For example, rose as a bloom and rose as rose got up. );

Large percentage of abbreviations

Lexical diversity;

large average sentence length.

Overview and comparison of NLP tools

Use pre-made toolkits to begin NLP development. These platforms come with libraries and training on corpora. They also help you get started in text processing, particularly with the support of communities and large tech companies. Two types are important to know.

Open-source toolkits. SpaCy and NLTK offer a wide range of pre-trained models and resources, and are both free and flexible. However, they are intended for experienced coders with a good understanding of ML. You might want to consider the second option if you are just starting out in data science.

APIs that are MLaaS. Amazon, Google and Microsoft offer highly automated APIs. They don't require any knowledge of machine learning and can be easily integrated into your workflow. Because they are paid, developers and researchers prefer them over the enterprises. You can find a lot of information about MLaaS and APIs from these companies in our section.

We will be presenting an overview of some popular open-source NLP toolkits in this article.

NLTK -- A base to any NLP project

Natural Language Toolkit is a platform that allows Python projects to be built. It's well-known for its huge corpora and abundance of libraries as well as detailed documentation. Although community support is a great bonus, it can also be very useful. NLTK is a tool that can be used to perform text analysis. However, it does not contain sufficient data for deep learning. It will however be an excellent base for any NLP project that is augmented with other tools.

Genism -- a collection of word vectors

Genism is another Python library. It was designed for unsupervised information extraction tasks, such as topic modelling, document indexing, similarity retrieval, and document indexing. However, it is most commonly used for word vectors using integration with Word2Vec. This tool is known for its efficiency and memory optimization capabilities, which allow it to effortlessly handle large text files. However, it is not a complete package and should be used with SpaCy and NLTK.

How GTS can help you?

Global Technology Solutions understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text datasets, ADAS data collection and Video datasets are among the datasets we offer. We offer services in over 200 languages.