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).

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.

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.

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.

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

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.

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.