1. Classification

• Emotion Detection: This is a classification task that aims to detect the sentiment or emotional tone in text (e.g., positive, negative, neutral). Common models include machine learning classifiers like SVM, Naive Bayes, and deep learning models like CNNs and LSTMs.

• Detection of Fake News: This task is also a classification problem where the goal is to classify news as either "true" or "fake." Models used for this task include text classification models and more advanced models like BERT.

• Stance Detection: The goal of stance detection is to determine the stance (e.g., support, against, neutral) expressed in a text towards a particular target. This is a classification task that often uses traditional machine learning models like logistic regression, SVM, or deep learning models.

• Language Identification: This task involves determining which language a given text is written in. It is a multi-class classification problem. Models include feature-based classifiers (e.g., SVM) or deep learning models.

• Email Filtering: Email filtering is a binary classification task (spam vs. non-spam). The task can be solved using feature extraction and classification models, such as decision trees, random forests, and SVM.

• Text Summarization: Although text summarization is generally a generation task, extractive summarization (selecting key portions of the text) can also be viewed as a classification task, where the model classifies the importance of sentences.

• Sentiment Generation & Control: This task involves generating text with a specific emotional tone (positive, negative, etc.). While primarily a generation task, it also relies on classification to assign sentiment labels to generated text.

2. Regression

• Automated Essay Scoring: This is a regression task where the goal is to predict the score for an essay. The score is usually a continuous value. Regression algorithms like linear regression or SVM regression (SVR), or deep learning models, can be used for prediction.

3. Clustering

• Event Detection: Event detection involves clustering pieces of text that describe similar events, helping to identify and group different events described in the text. Common clustering algorithms include K-means and DBSCAN.

4. Generative

• Machine Translation: Machine translation involves translating text from one language to another. This is a generative task, often solved using sequence-to-sequence models like Seq2Seq, Transformer, which follow an encoder-decoder architecture to generate the translated text.

• Text Generation: Text generation aims to generate coherent and contextually relevant text based on some input text. Models like RNNs, LSTMs, and Transformers are typically used to perform this task.

• Text-to-Speech (TTS): The TTS task involves generating speech from input text. This is a generative task, and models like WaveNet and Tacotron are commonly used for generating high-quality speech from text.

• Dialog Systems: Dialog systems generate conversational responses to interact with users. These systems often rely on generative models, such as Transformer-based models like GPT, to create contextually appropriate responses.

• Sentiment Generation & Control: This is an extension of the generative task where the goal is to generate text with a specific sentiment (positive, negative, etc.).

5. Sequence Labeling

• Information Extraction: This task relies on sequence labeling techniques to identify specific entities in the text (such as names, locations, dates) and extract the relevant relationships. Common models include CRFs (Conditional Random Fields) and BiLSTM-CRF.

• Emotion Detection (Partially): Emotion detection can also be viewed as a sequence labeling task where each sentence or phrase is labeled with its sentiment or emotional state.

• Named Entity Recognition (NER): NER is a typical sequence labeling task that aims to identify named entities in text, such as people's names, locations, and organizations.

6. Other Methods

• Speech-to-Text (Speech Recognition): This task involves converting spoken language into text. Typically, sequence-to-sequence models like RNNs or LSTMs are used in combination with acoustic models to transcribe speech into written form.

• Text-to-Speech (TTS): This is a generative task where the model generates speech from text. Models like WaveNet and Tacotron are commonly used for this purpose.

• Question/Answering Systems (Q&A): Q&A systems often combine information retrieval, extraction, and generation. Popular models like BERT and GPT are used for answering questions based on a given context or text.

• Automated Essay Scoring: This is a regression task where essays are scored based on various criteria such as structure, fluency, and grammar. Deep learning models can predict scores for essays based on these factors.

• Cross-Lingual Information Retrieval: This task involves retrieving information across multiple languages, often using multi-language models that can handle cross-lingual tasks. This usually combines classification and information retrieval techniques.

• Multimodal Learning: Multimodal learning involves combining text with other types of data, such as images, video, or audio. For example, Visual Question Answering (VQA) combines visual and textual information for reasoning tasks.

• Knowledge Graphs & Graph Neural Networks: Knowledge graphs store structured information about entities and their relationships. Graph neural networks are used to perform reasoning tasks on the graph structure. These tasks often combine NLP with graph-based models.

• Semantic Understanding & Reasoning: Tasks like Natural Language Inference (NLI) and commonsense reasoning fall into this category. These tasks require deep semantic understanding and the ability to make logical inferences from text.