Explanation:
Named entity recognition (NER) is a technique used in natural language processing (NLP) to identify and extract entities, such as people, organizations, locations, and other important information from unstructured text data. The main objective of NER is to classify words or phrases in text into predefined categories or labels based on their meaning or significance. By identifying and extracting named entities, NER can help to improve the accuracy and efficiency of various NLP applications, such as information retrieval, sentiment analysis, and machine translation.
Explanation:
Stemming reduces a word to its root or base form in text mining and NLP. It is a common technique to preprocess text data before running machine learning algorithms. The goal of stemming is to reduce the number of unique words in the text data, which can help improve text analysis's efficiency and effectiveness.
Explanation:
The correct order for preprocessing in Natural Language Processing is as follows:
1. Tokenization
2. Case folding (lowercasing)
3. Stopword removal
4. Stemming or Lemmatization
Explanation:
Machine Translation is the process of automatically translating one natural language into another using computer algorithms and models. It involves using computational techniques to analyze and understand the meaning of the source language and generate equivalent sentences in the target language. The goal is to enable communication between people who speak different languages without requiring human translators to perform the translation manually.
