Parts-of-speech tagging:

Parts-of-speech (POS) tagging is a process in natural language processing (NLP) that involves labeling each word in a sentence with its corresponding part of speech, such as noun, verb, adjective, adverb, etc. POS tagging is an essential step in many NLP tasks because it helps computers understand the grammatical structure of text and extract meaningful information.

In brief, POS tagging involves the following steps:

1. Tokenization: The text is divided into individual words or tokens, which serve as the basic units of analysis.

2. Linguistic Analysis: Each word is analyzed based on its context, surrounding words, and grammatical rules to determine its most likely part of speech.

3. Tagging: A POS tag is assigned to each word based on the analysis conducted in the previous step. Common POS tags include NN (noun), VB (verb), JJ (adjective), RB (adverb), PRP (pronoun), and so on.

4. Ambiguity Resolution: Some words may have multiple possible parts of speech depending on their context. POS tagging algorithms often use contextual information and statistical models to disambiguate such cases and assign the most likely tag to each word.

Word sense disambiguation:

Word sense disambiguation (WSD) is a crucial task in natural language processing (NLP) that aims to determine the correct meaning or sense of a word within a particular context. Many words in natural language have multiple meanings, or senses, and identifying the intended sense of a word in a given context is essential for accurate language understanding and interpretation.

In brief, word sense disambiguation involves the following steps:

1. Context Identification: The first step is to identify the context surrounding the ambiguous word. This context may include nearby words, phrases, syntactic structures, and semantic relationships within the text.

2. Sense Inventory: A sense inventory is a collection of different meanings or senses associated with the ambiguous word. This inventory could be manually created, such as in the case of WordNet, or generated automatically based on large text corpora.