"In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context."
Labeling each word in a sentence with its part of speech, such as noun, verb, or adjective.
Parts of Speech (POS): Understanding the different categories of words in a language, such as nouns, verbs, adjectives, adverbs, etc.
Morphology: Study of the internal structure of words and their variations based on grammatical features such as tense, number, gender, etc.
Syntax: The arrangement of words in a sentence and the rules that govern their relationships.
Corpus Linguistics: An analysis of language based on large collections of text documents or corpora.
Machine Learning: Techniques used for computers to learn from data and improve their performance, including supervised and unsupervised learning, deep learning, decision trees, and logistic regression.
Rule-Based Methods: Creating rules based on language knowledge that can be used to assign tags to words.
Hidden Markov Models (HMM): A statistical model used to predict the probability of a sequence of events based on a sequence of observations.
Conditional Random Fields (CRF): A discriminative model used for sequential data, including Part-of-Speech Tagging.
Named Entity Recognition (NER): Identifying specific entities in text, such as names of people, organizations, places, and dates.
Contextual Features: Incorporating contextual information such as word frequency, neighboring words, and sentence structure to improve Part-of-Speech Tagging accuracy.
Error Analysis: Analyzing errors in Part-of-Speech Tagging and identifying ways to improve accuracy.
Evaluation Metrics: Methods for measuring the accuracy of Part-of-Speech Tagging models, such as precision, recall, and F1 score.
Multi-Lingual POS Tagging: Extension of POS tagging to multiple languages, including challenges and solutions.
Deep Learning Techniques: Application of deep neural networks, such as recurrent and convolutional neural networks, to Part-of-Speech Tagging.
Linguistic Annotation: Creating manual or automated annotations on text data to improve the quality of Part-of-Speech Tagging.
Word Sense Disambiguation (WSD): Determining the correct sense of a word based on its context.
Cross-Linguistic POS Transfer: Transfer of Part-of-Speech Tagging knowledge from one language to another.
Part-of-Speech Tagging Applications: Real-world applications of Part-of-Speech Tagging, such as in machine translation, sentiment analysis, and speech recognition systems.
Noun (N): A word that denotes a person, place, thing, idea or concept.
Verb (V): A word that denotes an action, occurrence or state of being.
Adjective (Adj): A word that modifies a noun or a pronoun, giving more information on its quality, quantity or appearance.
Adverb (Adv): A word that modifies a verb, adjective or another adverb, indicating when, where, how, why or to what extent.
Pronoun (Pron): A word that replaces a noun or noun phrase, indicating the speaker, the listener or the object of the sentence.
Preposition (Prep): A word that shows the relationship between a noun or pronoun and other words in a sentence, indicating position, direction, time, or manner.
Conjunction (Conj): A word that connects words, phrases or clauses, indicating relationships of coordination, subordination or contrast.
Determiner (Det): A word that introduces a noun and clarifies or limits its meaning, indicating definiteness, indefiniteness or quantity.
Interjection (Intj): A word that expresses strong emotion or surprise, indicating joy, pain, approval, disapproval or indifference.
Participle (Part): A verb form that functions as an adjective or noun, indicating that the action or state of being is in progress, completed or perfect.
Infinitive (Inf): A verb form that functions as a noun, adjective or adverb, indicating that the action or state of being is hypothetical or indefinite.
Gerund (Ger): A verb form that functions as a noun, indicating that the action or state takes on the characteristics of a noun.
Modal verb (Mod): A special type of verb that expresses attitudes, possibility, necessity or ability, indicating degrees of certainty, obligation or permission.
"A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc."
"Once performed by hand, POS tagging is now done in the context of computational linguistics, using algorithms which associate discrete terms, as well as hidden parts of speech, by a set of descriptive tags."
"POS-tagging algorithms fall into two distinctive groups: rule-based and stochastic."
"E. Brill's tagger, one of the first and most widely used English POS-taggers, employs rule-based algorithms."
"...marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context."
"The process of marking up a word in a text (corpus) as corresponding to a particular part of speech..."
"POS tagging is now done in the context of computational linguistics..."
"Once performed by hand, POS tagging is now done in the context of computational linguistics..."
"...using algorithms which associate discrete terms, as well as hidden parts of speech, by a set of descriptive tags."
"(POS tagging or PoS tagging or POST), also called grammatical tagging..."
"[The process is] based on both its definition and its context."
"A simplified form of this is commonly taught to school-age children..."
"Once performed by hand..."
"...one of the first and most widely used English POS-taggers..."
"...identification of words as nouns, verbs, adjectives, adverbs, etc."
"POS-tagging algorithms fall into two distinctive groups: rule-based and stochastic."
"...now done in the context of computational linguistics..."
"...algorithms which associate discrete terms..."
"...as well as hidden parts of speech, by a set of descriptive tags."