Quote: "Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics."
The use of machines to manipulate and analyze natural language texts, including sentiment analysis and text classification.
Linguistics: The study of language and its structure, including syntax, semantics, phonetics, and pragmatics.
Probability and statistics: The use of statistical models to analyze natural language data, such as frequency distributions, correlations, and regression analysis.
Machine Learning: The use of algorithms and statistical models to identify patterns in natural language data, such as clustering, classification, and regression.
Neural Networks: A class of machine learning algorithms that can learn from massive amounts of data to make predictions or identify patterns, similar to how the human brain works.
Deep Learning: A subset of neural networks that utilizes multiple layers of processing to learn complex representations of data, which can be applied to natural language processing tasks like named entity recognition or sentiment analysis.
Text Mining: The process of extracting insights and information from large volumes of unstructured text data like articles, social media data or weblogs.
Information Retrieval: The process of finding relevant information from a large set of documents, like search-engines or intelligent chatbots.
Natural Language Generation: Using software to generate human-readable text like for customer service responses and chatbots.
Sentiment analysis: A sub-field of natural language processing that deals with identifying and classifying sentiment, opinions, and emotions in textual data using machine learning techniques.
Named Entity Recognition: Recognition of named entities such as names of persons, locations or organizations in a text document.
Topic modeling: A statistical technique used to identify the topics that are discussed in a large corpus of text data.
Language modelling: Building models that produce sequences of words that are similar to a given set of texts to handle language variations.
Information Extraction: Extracting specific information from semi-structured or unstructured data, such as named entities, events, or relations.
Semantic analysis: A subset of natural language processing that aims to understand the meaning of text data by analyzing relationships between concepts.
Transfer learning: A technique where a pre-trained model on one natural language processing task is used as a starting point for training another model on a new task.
Data Preprocessing: Cleaning and annotating raw text data to make it ready for analysis.
Word Embeddings: A form of dimensionality reduction technique used to represent words in a vector space.
Grammar: The set of structural rules governing the composition of sentences, used to analyze and generate grammatically correct sentences.
Lexical Analysis: Identification of individual words or terms in a text document.
Morphological analysis: The study of the structure of words and the way that grammatical information is encoded in them.
Sentiment Analysis: Analyzing the sentiments behind text, be it positive or negative.
Named Entity Recognition (NER): Identifying the named entities in a given text like people, places, and organizations.
Topic Modeling: Identifying the topics of a given text.
Text Classification: Categorizing texts into different categories based on predefined classes.
Speech Recognition: Converting speech/audio to text.
Speech Synthesis: Converting text to speech/audio.
Machine Translation: Translating a given text from one language to another.
Text Summarization: Generating a short summary of a longer text.
Natural Language Generation (NLG): Generating a natural language output from structured data.
Natural Language Understanding (NLU): Understanding natural language input and converting it into a machine-readable format.
Question Answering: Answering questions posed by people based on the given text.
Named Entity Disambiguation: Identifying the correct entity out of several entities with the same name.
Coreference Resolution: Identifying the entities that refer to the same person or thing in a given text.
Sentiment Classification: Classifying texts as positive, negative or neutral.
Text Clustering: Grouping texts based on their similarities.
Intent Recognition: Identifying the intent of a given text.
Dialogue System: Generating a natural language conversation between a machine and a human.
Text Normalization: Converting text to a standardized format.
Dependency Parsing: Identifying the relationships between words in a sentence.
Syntactic Parsing: Identifying the structure of a sentence.
Lexical Semantics: Analyzing the meanings of words.
Discourse Analysis: Analyzing the structure of longer texts.
Information Extraction: Extracting relevant information from a given text.
Opinion Mining: Analyzing the opinions expressed in a given text.
Contextual Ambiguity Resolution: Resolving ambiguity in a given text by analyzing the context.
Quote: "It is primarily concerned with giving computers the ability to support and manipulate speech."
Quote: "It involves processing natural language datasets, such as text corpora or speech corpora."
Quote: "It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches."
Quote: "The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them."
Quote: "The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves."
Quote: "Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation."
Quote: "Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics."
Quote: "Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics."
Quote: "It is primarily concerned with giving computers the ability to support and manipulate speech."
Quote: "It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches."
Quote: "The technology can then accurately extract information and insights contained in the documents."
Quote: "The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them."
Quote: "The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves."
Quote: "It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches."
Quote: "It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches."
Quote: "Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation."
Quote: "Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics."
Quote: "It involves processing natural language datasets, such as text corpora or speech corpora."
Quote: "The technology can then accurately extract information and insights contained in the documents."