Reference: https://en.wikipedia.org/wiki/Part-of-speech_tagging

Definition of POS Tagging

Tian, Yuanhe & Song, Yan & Ao, Xiang & Xia, Fei & Quan, Xiaojun & Zhang, Tong & Wang, Yonggang. (2020). Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge. 8286-8296. 10.18653/v1/2020.acl-main.735. Retrieved from https://www.aclweb.org/anthology/2020.acl-main.735.pdf

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge

Vyas, Yogarshi & Gella, Spandana & Sharma, Jatin & Bali, Kalika & Choudhury, Monojit. (2014). POS Tagging of English-Hindi Code-Mixed Social Media Content. 974-979. 10.3115/v1/D14-1105. Retrieved from https://www.aclweb.org/anthology/D14-1105.pdf

POS Tagging of English-Hindi Code-Mixed Social Media Content

Ball, Kelsey & Garrette, Dan. (2018). Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification. 3084-3089. 10.18653/v1/D18-1347.  Retrieved from https://www.aclweb.org/anthology/D18-1347.pdf

Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification

Jamatia, Anupam & Gambäck, Björn & Das, Amitava. (2015). Part-of-Speech Tagging for Code-Mixed English-Hindi Twitter and Facebook Chat Messages. 
Retrieved from https://www.aclweb.org/anthology/R15-1033.pdf

Part-of-Speech Tagging for
Code-Mixed English-Hindi Twitter and Facebook Chat Messages

Research papers, articles and examples about POS Tagging in NLP

University of Michigan - Ann Arbor

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. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. 

Part-of-speech Tagging

Part-of-speech Tagging References

Decoding, in models such as HMM that contains hidden variables, is the task that takes as input an HMM $\lambda = (A, B)$ and a sequence of observations $O = o_1 , ..., o_T$, and finds the most probable sequence of states $Q = q_1...q_T$. 

For part-of-speech tagging, the goal of HMM decoding is to choose the tag sequence $t_1...t_n$ that is most probable given the observation sequence of n words $w_1...w_n$:
$$\hat t_{1:n} = \argmax_{t_1, ..., t_n} P(t_1...t_n|w_1...w_n)$$



HMM Tagging as Decoding

State-of-the-art and human ceiling for part-of-speech tagging is 97%, while the baseline method (Most Frequent Class Baseline) has a comparable results of 92% on the WSJ Corpus (English).

Accuracy of Part-of-speech Tagging

Given an ambiguous word, choose the part-of-speech (POS) tag which is most frequent in the training corpus. Thus, a POS Classifier's performance can be compared with this baseline method.

Most Frequent Class Baseline

14%-15% of the vocabulary are words with 2 or more part-of-speech tags, while in running text (word tokens), 55-67% are words are ambiguous.

Words with Ambiguous Part-of-speech

For an input sequence of words [x1, x2, ... xn], each item is assigned its part-of-speech correctly. This is a disambiguation task as one word can have more than one potential part-of-speech.

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