What is AI’s Natural Language processing. What does it involved and provide some examples of it. Provide examples and present your written findings. Requirement APA format. You must include 3 scholarly reviewed references that are DIRECTLY related to the subject. Pages : 3 ( 1000 words)
AI’s Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves the development of algorithms and models to enable computers to understand, interpret, and generate natural language. NLP has become increasingly important as it plays a critical role in a wide range of applications, such as machine translation, information retrieval, sentiment analysis, and question answering systems.
NLP involves several tasks that contribute to the understanding and generation of human language. Some of the key tasks in NLP include:
1. Text Tokenization: In this process, a given text is divided into individual units such as words or sub-words (tokens). Tokenization serves as the first step towards understanding the semantic and syntactic structure of the text.
Example: The sentence “I love to play soccer” can be tokenized into the individual words: [“I”, “love”, “to”, “play”, “soccer”].
2. Part-of-Speech (POS) Tagging: POS tagging involves assigning a grammatical tag (e.g., noun, verb, adjective) to each word in a text. This information helps in understanding the role and context of a word within a sentence.
Example: The sentence “I have a black cat” can be POS-tagged as: [“I/PRON”, “have/VERB”, “a/DET”, “black/ADJ”, “cat/NOUN”].
3. Named Entity Recognition (NER): NER aims to identify and classify named entities, such as names of people, organizations, locations, and dates, within a given text. It helps in extracting structured information from unstructured text.
Example: In the sentence “Google was founded by Larry Page and Sergey Brin on September 4, 1998, in California”, NER would extract the named entities as: [“Google/ORG”, “Larry Page/PERSON”, “Sergey Brin/PERSON”, “September 4, 1998/DATE”, “California/LOC”].
4. Sentiment Analysis: Sentiment analysis, also known as opinion mining, involves determining the sentiment or subjective polarity (e.g., positive, negative, or neutral) expressed in a text. It is useful for understanding public opinion, product reviews, and social media sentiment.
Example: Analyzing the sentence “The movie was fantastic and highly recommended!” would yield a positive sentiment.
5. Machine Translation: Machine translation involves automatically translating text from one language to another. It is a complex task that involves understanding the source language and generating the equivalent text in the target language.
Example: Translating the English sentence “Hello, how are you?” to French would yield “Bonjour, comment ça va ?”.
These are just a few examples of the many tasks encompassed by NLP. To tackle these tasks, various machine learning and deep learning techniques are employed, including statistical models, neural networks, and transformers. These models are trained on large amounts of annotated data to learn patterns and associations between words and their meanings.
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