The correct answer is D. Parts-of-Speech tagging determines all part-of-speech for a specific word given as input, dynamically as per the meaning of the sentence and the sentence structure.
Parts-of-Speech tagging is the process of assigning a part-of-speech tag to each word in a sentence. Part-of-speech tags are labels that indicate the grammatical role of a word in a sentence. For example, the word “the” is a determiner, the word “dog” is a noun, and the word “ran” is a verb.
Parts-of-Speech tagging is important because it helps to disambiguate words that have multiple meanings. For example, the word “bank” can be a noun (a financial institution) or a verb (to deposit money). Knowing the part-of-speech of a word can help to determine its meaning in a particular context.
Parts-of-Speech tagging can be done manually or automatically. Manual tagging is time-consuming and labor-intensive, but it is the most accurate method. Automatic tagging is faster and easier, but it is not always as accurate as manual tagging.
There are a number of different algorithms that can be used for automatic Parts-of-Speech tagging. Some of the most common algorithms include rule-based algorithms, statistical algorithms, and neural network algorithms.
Rule-based algorithms use a set of rules to assign part-of-speech tags to words. Statistical algorithms use statistical methods to learn the probability that a word belongs to a particular part-of-speech. Neural network algorithms use artificial neural networks to learn the relationship between words and part-of-speech tags.
Parts-of-Speech tagging is a valuable tool for natural language processing. It can be
used for a variety of tasks, such as text analysis, machine translation, and speech recognition.