The key steps involved in basic text processing in natural language processing are:

1. Tokenization: This is the process of breaking down the text into individual words or tokens. This is the first step in text processing.

2. Stop words removal: Stop words are common words that do not contribute much to the meaning of a sentence, such as "the", "is", "in", etc. These words are often removed to reduce the size of the data and improve processing time.

3. Stemming: This is the process of reducing inflected (or sometimes derived) words to their word stem or root form. For example, "jumps", "jumped", "jumping" are all stemmed to the root word "jump".

4. Lemmatization: This is similar to stemming but it takes into consideration the morphological analysis of the words. The aim is to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma.

5. Bag of Words (BoW) model: This is a method of extracting features from text for use in modeling. A bag-of-words is a representation of text that describes the occurrence of words within a document.

6. TF-IDF: This stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic that reflects how important a word is to a document in a collection or corpus.

7. Word embeddings: This is the collective name for a set of language modeling and feature learning techniques in natural language processing where words or phrases from the vocabulary are mapped to vectors of real numbers.

8. Named Entity Recognition (NER): This is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

9. Part-of-speech tagging: This involves labeling each word in a sentence with its appropriate part of speech (noun, verb, adjective, etc.).

10. Syntax parsing: This is the process of analyzing a string of symbols, either in natural language or in computer languages, according to the rules of a formal grammar.

11. Machine translation: This is the process of translating text from one language to another.

12. Sentiment analysis: This involves determining the attitude, sentiments, evaluations, appraisals, etc. of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.

13. Topic modeling: This is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.

Question

The key steps involved in basic text processing in natural language processing are:

1. Tokenization: This is the process of breaking down the text into individual words or tokens. This is the first step in text processing.

2. Stop words removal: Stop words are common words that do not contribute much to the meaning of a sentence, such as "the", "is", "in", etc. These words are often removed to reduce the size of the data and improve processing time.

3. Stemming: This is the process of reducing inflected (or sometimes derived) words to their word stem or root form. For example, "jumps", "jumped", "jumping" are all stemmed to the root word "jump".

4. Lemmatization: This is similar to stemming but it takes into consideration the morphological analysis of the words. The aim is to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma.

5. Bag of Words (BoW) model: This is a method of extracting features from text for use in modeling. A bag-of-words is a representation of text that describes the occurrence of words within a document.

6. TF-IDF: This stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic that reflects how important a word is to a document in a collection or corpus.

7. Word embeddings: This is the collective name for a set of language modeling and feature learning techniques in natural language processing where words or phrases from the vocabulary are mapped to vectors of real numbers.

8. Named Entity Recognition (NER): This is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

9. Part-of-speech tagging: This involves labeling each word in a sentence with its appropriate part of speech (noun, verb, adjective, etc.).

10. Syntax parsing: This is the process of analyzing a string of symbols, either in natural language or in computer languages, according to the rules of a formal grammar.

11. Machine translation: This is the process of translating text from one language to another.

12. Sentiment analysis: This involves determining the attitude, sentiments, evaluations, appraisals, etc. of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.

13. Topic modeling: This is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.

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