Explain the phases NLP.

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1. lexical Analysis and Morphological: The first phase of NLP is the Lexical Analysis. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It divides the whole text into paragraphs, sentences, and words.

2. Syntactic Analysis (Parsing): Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words. Example: Arjun goes to the Radha In the real world, Arjun goes to the Radha, which does not make any sense, so this sentence is rejected by the Syntactic analyzer. 

 3. Semantic Analysis: Semantic analysis is concerned with the meaning representation. It mainly focuses on the literal meaning of words, phrases, and sentences.

4. Discourse Integration: Discourse Integration depends upon the sentences that proceed it and also invoke the meaning of the sentences that follow it.

5. Pragmatic Analysis: Pragmatic is the fifth and last phase of NLP. It helps you to discover the intended effect by applying a set of rules that characterize cooperative dialogues. For Example: "Open the door" is interpreted as a request instead of an order.



Comments

  1. for.April 15, 2026 at 6:24 AM

    great

    ReplyDelete
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    1. for.April 15, 2026 at 6:27 AM

      Natural Language Processing (NLP) is a field of Artificial Intelligence that enables machines to understand, interpret, and generate human language. The NLP process typically follows a series of structured phases that transform raw text into meaningful insights.


      NLP Text Generation Projects For Final Year


      🔹 1. Text Acquisition

      This is the initial phase where raw text data is collected from sources such as documents, websites, social media, or speech (converted to text).

      🔹 2. Text Preprocessing

      Raw text is cleaned and prepared for analysis. This includes:

      Tokenization (splitting text into words/sentences)
      Removing stop words (e.g., “the”, “is”)
      Stemming and lemmatization
      Lowercasing and noise removal

      👉 This phase ensures the data is consistent and usable.

      🔹 3. Lexical Analysis

      In this stage, the text is analyzed at the word level:

      Identifying word structures
      Understanding vocabulary
      Assigning basic meanings
      🔹 4. Syntactic Analysis (Parsing)

      This phase examines the grammatical structure of sentences:

      Part-of-Speech (POS) tagging
      Sentence parsing
      Checking grammatical correctness

      👉 Helps the system understand how words are arranged.

      🔹 5. Semantic Analysis

      Here, the focus is on understanding meaning:

      Word sense disambiguation
      Named entity recognition
      Context interpretation

      👉 Converts text into meaningful representations.

      🔹 6. Pragmatic Analysis

      This advanced phase interprets context and intent:

      Understanding sarcasm, tone, and intent
      Considering real-world knowledge
      🔹 7. Feature Extraction / Representation

      Text is converted into numerical form for machine learning:

      Bag of Words (BoW)
      TF-IDF
      Word embeddings (Word2Vec, GloVe)
      🔹 8. Modeling & Prediction

      Machine learning or deep learning models are applied:

      Classification (sentiment analysis)
      Translation
      Chatbots
      🔹 9. Evaluation & Output

      The final phase evaluates model performance using metrics like:

      Accuracy
      Precision & Recall
      F1-score

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