Related Questions:
– What are some of the most common applications of NLP?
– What are Language Models? Discuss the evolution of Language Models over time
Natural Language Processing(NLP) is an inter-disciplinary field of Artificial Intelligence (AI) and Computational linguistics that focuses on the understanding of human language by computers. The goal of NLP tasks is to enable computers to understand, interpret, and generate human language (text or speech) in a way that is both meaningful and useful. It emphasizes contextual understanding rather than a standalone comprehension of individual words by machine learning algorithms.
NLP involves a range of tasks that revolve around processing and analyzing natural language (text or speech). Some of the key tasks in NLP include:
- Text Understanding:
- Tokenization: Breaking down text into individual words or tokens.
- Part-of-Speech Tagging: Identifying the grammatical parts of speech (e.g., nouns, verbs, adjectives) of words in a sentence.
- Named Entity Recognition: Identifying and classifying entities such as names of people, places, organizations, and more in a text.
- Parsing: Analyzing sentence structure to determine the grammatical relationships between words.
- Text Generation:
- Language Generation: Creating coherent and contextually appropriate sentences and paragraphs of text.
- Machine Translation: Automatically translating text from one language to another.
- Text Classification and Sentiment Analysis:
- Sentiment Analysis: Determining the emotional tone or sentiment expressed in a piece of text (e.g., positive, negative, neutral).
- Text Classification: Categorizing text into predefined classes or categories (e.g., spam detection, topic classification).
- Language Understanding and Dialog Systems:
- Question Answering: Providing relevant answers to questions posed in natural language.
- Chatbots and Conversational Agents: Interacting with users in a natural and human-like manner.
- Text Summarization and Extraction:
- Text Summarization: Generating concise summaries of longer texts.
- Information Extraction: Identifying and extracting specific information (e.g., dates, locations, events) from text.
NLP isn’t limited to written text though. It also tackles complex challenges in speech recognition and computer vision, such as generating a transcript of an audio sample or a description of an image. There are several other terms that are roughly synonymous with NLP, such as NLU and NLG. NLU, or natural language understanding and NLG, or natural language generation (NLG) refer to using computers to understand and produce human language, respectively. In common practice, NLP has become a catch-all phrase for any task involving processing of human language data.
Video Explanation
- The video titled “NLP in 10 mins” by Edureka succinctly and clearly explains the meaning of Natural Language Processing and its applications (Runtime: 9 mins)
- This is another great NLP video by Crashcourse, which introduces the concept of NLP, walks you through the key developments in the field of NLP over time, and different NLP applications (Runtime: 12 mins)
https://www.youtube.com/watch?v=fOvTtapxa9c
