What's included

• Text Preprocessing

  Tokenization: Breaking down text into smaller units such as words, sentences, or paragraphs. Stopword removal: Removing common words that do not carry significant meaning (e.g., "the", "a", "is"). Stemming/Lemmatization: Reducing words to their base or root form. Normalization: Converting text to a consistent format (e.g., lowercase, removing punctuation).

• Named Entity Recognition (NER)

  Identifying and extracting named entities (e.g., people, organizations, locations, dates) from text. Classifying named entities into predefined categories. Improving NER performance through domain-specific training data and models.

• Text Classification

  Categorizing text documents into predefined classes or topics. Developing multi-class and multi-label classification models. Evaluating classification performance using metrics such as accuracy, precision, recall, and F1-score.

• Sentiment Analysis

  Determining the sentiment (positive, negative, or neutral) expressed in text. Implementing techniques such as lexicon-based and machine learning-based sentiment analysis. Providing sentiment scores or labels for text inputs.

• Text Summarization

  Generating concise summaries of longer text documents. Implementing extractive and abstractive summarization approaches. Evaluating summary quality using metrics like ROUGE and BLEU.

• Question Answering

  Developing models that can answer questions based on given text. Supporting both factual and open-ended questions. Providing accurate and relevant answers to user queries.

• Text Generation

  Generating coherent and contextually relevant text. Implementing language models for tasks like text completion and story generation. Ensuring generated text is grammatically correct and semantically meaningful.