View source for Part-of-Speech Tagging

== Introduction ==

Part-of-speech tagging, also known as [[Grammatical tagging|grammatical tagging]] or word-category disambiguation, is the task 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. This process is a fundamental step in the field of [[Natural Language Processing|Natural Language Processing (NLP)]].

[[Image:Detail-54379.jpg|thumb|center|A computer screen displaying a sentence with each word tagged with its corresponding part of speech.|class=only_on_mobile]]
[[Image:Detail-54380.jpg|thumb|center|A computer screen displaying a sentence with each word tagged with its corresponding part of speech.|class=only_on_desktop]]

== History ==

The concept of part-of-speech tagging has its roots in the early linguistic studies of [[Panini (grammarian)|Panini]], a Sanskrit grammarian who, around 500 BC, formulated 4,000 rules of [[Sanskrit morphology|Sanskrit morphology]]. The modern computational techniques for part-of-speech tagging were first seen in the mid-20th century with the advent of stochastic methods and [[Machine Learning|machine learning]] algorithms.

== Importance ==

Part-of-speech tagging is an essential component in many NLP tasks such as [[Parsing|parsing]], [[Text-to-Speech|text-to-speech conversion]], [[Information Extraction|information extraction]], and [[Machine Translation|machine translation]]. It provides the syntactic skeleton of a sentence, enabling more advanced language understanding tasks.

== Techniques ==

Part-of-speech tagging techniques can be broadly classified into rule-based, stochastic, and neural network based methods.

=== Rule-based Tagging ===

Rule-based tagging relies on handcrafted rules and linguistic knowledge. An example of such a system is the [[EngCG|Constraint Grammar]] developed by Karlsson in 1990. These systems use a set of handcrafted rules to determine the part of speech for each word.

=== Stochastic Tagging ===

Stochastic tagging uses statistical methods, particularly [[Hidden Markov Model|Hidden Markov Models (HMMs)]], to assign tags to words. The most common stochastic tagger is the [[Viterbi algorithm|Viterbi algorithm]] based tagger.

=== Neural Network Based Tagging ===

With the advent of [[Deep Learning|deep learning]], neural network based tagging methods have gained popularity. These methods use architectures like [[Recurrent Neural Network|RNNs]], [[Long Short-Term Memory|LSTMs]], and [[Transformer (machine learning model)|Transformers]] to predict the part of speech tags.

== Evaluation ==

The performance of a part-of-speech tagger is typically measured using the accuracy metric, which is the percentage of words that are correctly tagged. The [[Penn Treebank|Penn Treebank]] is a commonly used benchmark for evaluating the performance of POS taggers.

== Applications ==

Part-of-speech tagging has a wide range of applications in various fields of NLP, including:

* [[Information Retrieval|Information retrieval]]
* [[Machine Translation|Machine translation]]
* [[Speech Recognition|Speech recognition]]
* [[Sentiment Analysis|Sentiment analysis]]
* [[Text Summarization|Text summarization]]

== See Also ==

* [[Named Entity Recognition]]
* [[Dependency Parsing]]
* [[Lemmatization]]
* [[Stemming]]

[[Category:Natural Language Processing]]
[[Category:Computational Linguistics]]
[[Category:Artificial Intelligence]]