Artificial intelligence experts are constantly working to invent machines that perfectly replicate complicated tasks that only the human mind could accomplish in the past. One of the most essential tasks that human minds are capable of is the ability to create and understand complex languages. Languages are one of the main pillars on which humanity has progressed. Thus, language is one of the most discussed concepts for AI professionals. The past two decades have seen rapid natural language processing (NLP) progress.
Natural language processing (NLP) refers to the branch of computer science—specifically, the branch of artificial intelligence, or AI—that allows computers to understand text and spoken words in much the same way that human beings can.
NLP integrates statistical, machine learning, and deep learning models with computational linguistics, or the rule-based modeling of human language. Together, these technologies allow computers to process human language in the form of text or voice data and "understand" its whole meaning, including the intent and feelings of the speaker or writer.
Computer programs that translate text between languages, react to spoken commands, and swiftly summarize massive amounts of text—even in real-time—are all controlled by NLP. You've probably used NLP in speech-to-text software, voice-activated GPS systems, digital assistants, chatbots for customer support, and other everyday conveniences. NLP, however, also plays an increasing part in enterprise solutions that assist in streamlining corporate operations, boosting worker productivity, and streamlining crucial company procedures.
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Natural language processing requires algorithms to recognize and state natural language rules so that raw language data is transformed into a machine-understandable form. When we give a computer text, it uses algorithms to understand the meaning of each sentence and extract essential data from them. However, there are times when these computers cannot get the exact meaning of a sentence, which can lead to uncertain results.
For example, one of the common mistakes is translating the word "online" from English to Russian. In English, online means "connected to networks", but its Russian translation has a synonym that means "interactive".
Another example is a bot-based English sentence restructuring tool that translates a sentence so that it can change the entire meaning.
Get an idea of a wide range of NLP use cases with these example algorithms:
Summarize blocks of text using the Summarizer to capture the most important and central ideas and ignore irrelevant information.
Create a chatbot using Parsey McParseface, a deep learning language analysis model created by Google that uses point-of-speech tagging.
Generate keyword topic tags from a document using LDA (Latent Dirichlet Allocation), which determines the most relevant words from a document. This algorithm is the heart of the Auto-Tag and Auto-Tag URL microservices.
Identify the type of entity extracted, such as a person, place, or organization, using named entity recognition.
Based on StanfordNLP, Sentiment analysis can be used to identify a feeling, opinion, or belief from a statement, from very negative to neutral to very positive. Developers often use an algorithm to identify the sentiment of an expression in a sentence or use sentiment analysis to analyze social media.
Reduce words to their root or stem with PorterStemmer or split the text into tokens with Tokenizer.
Semantic and syntactic analysis are the two main techniques used to perform NLP-related tasks.
The term "syntax" describes how a bunch of words is placed together to form a meaningful sentence. This process is used in NLP to gauge how well grammar rules are supported by natural language.
In order to determine the true meaning of a group of words, some computer algorithms are employed to apply grammar rules to the words.
Several Syntactic Techniques include the following:
Lemmatization: Reduces the complexity of a word to make it easy to understand
Morphological Segmentation: Divides words into distinct parts
Word segmentation: Breaks large text into separate units to improve readability.
Parts of Speech: identifies the parts of speech for each individual word
Analysis: Checks the grammar of the sentence
Sentence Break: Limits the length of each sentence to large text.
Stemming: Removes edited words from their original form.
Semantics means the actual meaning of a given text. Semantic analysis is a complex natural language processing (NLP) procedure that is still not fully resolved.
In this process, some computer algorithms are used to analyze and understand the meaning of words and the order of sentences.
Critical methods of semantic analysis include:-
1. Voice Assistants and Chatbots
We are all aware that many websites now use chatbots and voice assistants like Alexa, Siri, and Google Assistant to assist and direct new visitors.
A voice assistant is a software that interprets voice commands from the user and responds appropriately using NLP and speech recognition. Similar to this, chatbots are computer programs created to assist users around the clock and effectively respond to any questions they may have.
The majority of chatbots and virtual assistants have pre-programmed response systems that react in accordance with predefined rules and patterns. Powerful Artificial Intelligence has enabled some voice assistants to communicate with the user and respond appropriately. They even improve themselves with more use. Assistants like Siri and Alexa can even converse with the user like an average person!
2. Automatic Completion in Search Engines
Have you ever noticed that search engines frequently auto-complete your sentences and try to predict what you're typing? If you put "game" into Google, for instance, you might also get suggestions for "game of thrones," "game of life," or, if you're into math, "game theory." All of these suggestions are offered using autocomplete, which employs natural language processing to infer what you're trying to ask. Search engines evaluate huge data sets to determine the most frequent possibilities that users are likely to type when they enter specific phrases. They interpret the meaning of these words and the relationships between them to create various phrases using natural language processing.
3. Language Translator
Do you want to translate text from English to Hindi but don't know Hindi? Google Translate is the tool for you! While it's not quite 100% accurate, it's still an excellent tool for converting text from one language to another. Google Translate and other translation tools also use sequence modeling, a natural language processing technique. It allows an algorithm to convert a sequence of words from one language to another, a translation. Previously, language translators used statistical machine translation (SMT), which meant they analyzed millions of documents that had already been translated from one language to another (in this case, English to Hindi) and then looked for common patterns and the underlying vocabulary of the language. However, this method was not as accurate compared to sequence-by-sequence modeling.
NLP and machine learning applications are crucial in supporting communication between machines and humans. With more research in this field, further developments are making machines smarter at learning and understanding human speech.
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