Applications of Natural Language Processing (NLP) and NLP Data Sets

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that deals with the interaction between humans and computers using natural language.
NLP data sets are used to train models that can then be used for various tasks such as text classification, entity recognition, machine translation, etc.
There are many different applications of NLP, and in this post we will take a look at some of the most popular and the importance of NLP data sets for training applications.

Table of Contents

• What is natural language processing (NLP)?

• History of NLP

• Common Applications of NLP

• How does NLP work?

• Challenges in Natural Language Processing (NLP)

• How do NLP data sets help the algorithm become better?

• Conclusion

What is natural language processing (NLP)?

Natural language processing or NLP, is the term used to describe how language is processed by machines. NLP is an area of artificial intelligence (AI). In their daily lives, people are coming into contact with AI programs that use NLP more and more often. Examples include utilizing Alexa at home, OK Google on their smartphone, or calling customer service. Nowadays, people communicate with machines more frequently. Additionally, NLP is being used in an increasing number of fields.

Computational linguistics, or the rule-based modeling of human language, is combined with statistical, machine learning, and deep learning models to form NLP. With the use of these technologies, computers are now able to process human language in the form of text or audio data and fully “comprehend” what is being said or written, including the speaker’s or writer’s intentions and sentiment.

NLP is used to analyze text so that computers can comprehend human speech. Real-world applications like as automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, connection extraction, stemming, and more are made possible by this human-computer interaction. Machine translation, text mining, and automated question-answering are all common uses for NLP.

History of NLP

Roughly speaking, research on natural language processing began in the 1950s, while there is some earlier work available. Alan Turing proposed what is now known as the Turing test as a standard of intelligence in a 1950 article titled “Computing Machinery and Intelligence.”

In addition, most NLP systems prior to the 1980s relied on intricate, handwritten rules. Machine learning (ML) techniques for language processing, however, led to a revolution in NLP beginning in the late 1980s. This was caused by both the slow decline in the supremacy of Chomskyan theories of linguistics, whose theoretical underpinnings hindered the kind of corpus linguistics that underlies the ML approach to language processing, and the continual development in computational capacity. Decision trees, one of the original ML algorithms, provided systems of strict if-then rules that were comparable to handwritten rules already in use.

Common Applications of NLP

Tools for natural language processing can be used to automate time-consuming tasks, analyze data and find insights, and gain a competitive edge.

1. Auto-correct

The widely used automatic data validation feature known as autocorrect, is frequently featured in word processors and text editing interfaces for smartphones and tablet computers. Software that performs auto-correct and grammatical checks heavily relies on natural language processing. By identifying grammar, spelling, and sentence structure issues, NLP is used to help you improve your writing.

2. Speech Recognition

Natural language processing is used in speech recognition technologies to convert spoken language into a machine-readable format. Virtual assistants like Siri, Alexa, and Google Assistant all require speech recognition technology.

3. Sentiment Analysis

Sentiment analysis, often known as opinion mining, is a technique used in natural language processing (NLP) to determine the emotional undertone of a document.
Businesses frequently do sentiment analysis on textual data to track the perception of their brands and products in customer reviews and to better understand their target market.

4. Chatbots

Software programs called chatbots mimic human conversation. In order to simulate real-world interactions and respond to customer inquiries, they adhere to a set of pre-designed rules. Additionally, chatbots employ artificial intelligence (AI) and Natural Language Processing (NLP) interpret these exchanges almost as well as a human.

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How does NLP work?

Beginning with straightforward word processing and moving on to recognizing complex phrase meanings, natural language processing is divided into five main stages or phases.

1. Step 1: Lexical Analysis

The first step in NLP is lexical or morphological analysis. It involves identifying and examining word structures. The term “lexicon” refers to a language’s body of words and expressions. A text file is dissected into paragraphs, phrases, and words using lexical analysis. In this stage, the source code is scanned as a stream of characters and transformed into readable lexemes. There are paragraphs, sentences, and words scattered throughout the entire book.

2. Step 2: Syntax Analysis

A method for examining links between words, arranging words, and evaluating grammar is called syntactic or syntax analysis. It requires looking at the syntax of the phrase’s words and arranging them to show how they relate to one another. The correct structure of a particular piece of text is ensured through syntax analysis. To check if the grammar is accurate at the sentence level, it attempts to parse the sentence. Based on the sentence structure and the likely POS produced in the previous stage, a syntax analyzer gives POS tags.

3. Step 3: Semantic Analysis

Semantic analysis is the process of determining a statement’s meaning. The attention is primarily on the literal meaning of words, phrases, and sentences. It also has to do with stringing words into coherent sentences. It takes the precise meaning or dictionary definition from the text. The text’s meaning is investigated. The task domain’s syntactic structures and objects are mapped to do this.

4. Step 4: Discourse Integration

“Discourse integration” is a concept that describes a sense of context. Any sentence’s meaning is defined by the meaning of the sentence that comes before it. It also establishes the meaning of the subsequent statement. The preceding sentences have an impact on how the speech is integrated. That is to say, that assertion or phrase depends on the previous phrase or sentence. The same holds true for the use of pronouns and proper nouns.

5. Step 4: Pragmatic Analysis

Pragmatic analysis is NLP’s final and fifth phase. Pragmatic analysis focuses on the whole communicative and social content and how it affects interpretation. You can use pragmatic analysis to find the desired outcome by using a set of rules that describe cooperative discussions. It addresses issues such as word repetition, who said what to whom, and other issues. It understands the context in which people converse with one another as well as a number of other elements. It alludes to the procedure of removing or abstracting the significance of the words used in a given circumstance. Using the information obtained in the earlier stages, it translates the text that is provided.

Challenges in Natural Language Processing (NLP)

1. Faulty Training Data

NLP is mainly about studying the language and to be proficient, it is important to spend a substantial amount of time listening, reading, and understanding training data. NLP systems focused inaccurate data learn inefficiently and incorrectly, thereby giving faulty results

2. Time Taken to Develop NLP Systems

An NLP system takes longer to develop overall. AI analyzes the data points in order to process and apply them appropriately. The deep networks and GPUs train on datasets that can be trained in a matter of hours. The already-existing NLP technology can assist in creating the product from the ground up.

3. Lack of Research and Development

The application of NLP is multifaceted. Instead, it needs assisting technologies like deep learning and neural networking to advance into something revolutionary. The lack of appropriate research and development tools frequently results in the rejection of this hack, which is a terrific approach to create unique models by adding tailored algorithms to particular NLP implementations.

How do NLP data sets help the algorithm become better?

Large data sets are needed to teach NLP applications for AI. This information may come from a variety of sources, such as chats, tweets, or other social media posts. But because they don’t fit into relational databases’ conventional architecture, NLP data sets are unstructured. Instead, these NLP data sets need to be categorized and examined. In this way, despite the fact that words themselves may suggest numerous meanings, robots can learn what is meant by any utterance. Thus, NLP data sets enable cognitive language understanding for AI applications. Different classifications can be made at the levels of syntax, semantics, discourse, and speech. These include things like lemmatization and stemming, as well as sentiment analysis, speech recognition, and text-to-speech.

Conclusion

NLP significantly improves the capabilities of AI systems, whether they are used to create chatbots, phone and email customer care, filter spam communications, or create dictation software. Systems that use chatbot NLP are very helpful when speaking with customers. The general guideline is that the results will be more accurate the larger the data base.

Robert Koch