NLP Natural Language Processing - Symetricx

Natural Language Processing

It is part of artificial intelligence that studies how machines interact with human language. Artificial intelligence is constantly working behind the scenes to improve many of the tools we use every day, such as NLP, Symetricx chatbot, Symetricx spell checker.

When NLP is combined with machine learning algorithms, it creates systems that learn to perform tasks on their own and get better through experience. Companies are increasingly using NLP-equipped tools to gain insights from data and automate routine tasks.

What is Natural Language Processing?

Natural language processing is an artificial intelligence application that gives computers the ability to read, understand and interpret human language. It helps computers measure human emotions and identify which parts of human language are important.

The goal of natural language processing (NLP) is to create systems that can make sense of text and perform tasks such as translation, grammar checking, or topic classification.

Virtual assistants such as Google Assist, Siri and Alexa are among the most popular examples of natural language processing applications in our lives. Another common use case for NLP is intelligent chatbots that help you solve problems while performing natural language generation.

In addition to these, there are many application areas of NLP that we encounter even in the tools we use every day, but that we probably do not even realize. For example, translation of a message written in a different language on channels such as Twitter, Facebook, Instagram, or text suggestions when filtering unwanted e-mails into spam files, etc.

How Natural Language Processing Works?

In its simplest definition, natural language processing primarily applies linguistics to analyze linguistic structure and meaning of words. It then uses different algorithms to create intelligent systems that can perform various tasks.

Key NLP tasks include tokenization and parsing, lemmatization/rooting, part of speech tagging, language perception, and identification of semantic relationships.

NLP in general terms; breaks down language into shorter, basic parts, tries to understand the relationships between the parts, and explores how the parts work together to create meaning.

Natural Language Processing Techniques

Natural Language Processing (NLP) applies two techniques to help computers understand texts: syntactic analysis and semantic analysis.

Syntactic Analysis

Syntactic analysis or parsing; Analyzes the text by using basic grammar rules to determine sentence structure, how words are arranged and how words relate to each other. Some of its duties are:

• Symbolization; It consists of breaking a text into smaller parts called symbols (which can be sentences or words) to make the text easier to process.

• Part of speech tagging; its symbols are verbs, adverbs, adjectives, nouns, etc. as tags. This helps to understand the meaning of a word (for example, the word “write” means different things when used as a verb or a noun).

• Lemmatization and rooting; reduces a word to its familiar basic form to facilitate analysis.

Semantic Analysis

Semantic analysis focuses on finding the meaning of the text. First, it examines the meaning of each word (lexical semantics). Then it looks at the combination of words and what they mean in context. The main subtasks of semantic analysis are:

• Disambiguation in the meaning of the word; tries to explain in what sense a word is used in a particular context.

• Relationship extraction; tries to understand how entities such as places, people, organizations, etc. are related to each other in the text.

NLP, AI, Machine Learning: What’s the Difference?

Natural Language Processing, Artificial Intelligence, and machine learning are sometimes used interchangeably, confusingly. First of all, the main point to know is that NLP and machine learning are subsets of artificial intelligence.

Artificial intelligence is a general term for systems that can simulate human intelligence. AI encompasses applications that mimic cognitive abilities such as learning from example and problem solving. This covers many different applications, from driverless cars to forecasting systems.

Natural language processing deals with how computers understand and translate human language. With NLP, systems can make sense of written or spoken text and perform tasks such as translation, keyword extraction, topic classification, and more.

Machine learning is needed to automate all these processes and provide accurate answers. Machine learning is the process of applying algorithms that teach systems how to learn and improve automatically from experience without being explicitly programmed. For example, AI-powered chatbots use NLP to interpret what users are saying and what they want to do, and machine learning to automatically give more accurate answers by learning from past interactions.

Identifying the features of a language is important for the development of Natural Language Processing Systems such as automatic language detection, detection of misspelled words in text, spelling of words, and automatic text summary. However, knowing the language features for optical character recognition, cryptology, data compression, voice synthesis and recognition is of great benefit. However, developing a natural language processing system for the Turkish language is difficult due to the structure of the language. We need different techniques to overcome the difficulties arising from the nature of language. In this sense, this study proposes a new approach for the spelling and statistics of Turkish words. Statistical n-gram language models were created to extract syllable statistics.

The probability of succession of syllables in words in the Turkish corpus was calculated with statistical language models. Studies that perform word-based approaches in statistical language models are not suitable for Turkish. Therefore, it is thought that it is more convenient to use syllable-based approaches rather than word-based studies.

Statistical language models are frequently used to calculate the probabilities of a sentence or words in a sentence.

With the developed Symetricx Nlp system, the idea has emerged that compression algorithms can be developed for Turkish texts and that it will enable the detection of typos that may occur in Turkish text writing. It also formed the basis for the system’s syllable-based Turkish sound synthesis and speech recognition systems.