Overview

Hate Speech

1) Clarify requirements, Frame as ML problem: Sentence Classification on Hate 2) Data: Labelled data with Hate/Not Hate

  • Data preprocessing: remove stop words, lowercase, remove punctuations, remove stop words, tokenization: breaking words down into smaller tokens: subword, word peice, sentencepiece
  • Data: Imbalance? Augmentation strategies, undersample, oversampling 3) Models:

Transformer: BERT

  • Input: Tokenized text data; each token is converted into an input ID, with special tokens [CLS] and [SEP] added.
  • Output: The probability distribution over classes (e.g., hate speech or not) for classification.
  • Layers Added:
    • Classification head: A fully connected (Dense) layer with an output size equal to the number of classes.
    • Dropout layer before the classification layer to prevent overfitting.
  • Loss Function: Categorical cross-entropy for multi-class classification or binary cross-entropy for binary classification.
  • Regularization:
    • Dropout in the classification head.
    • L2 regularization can also be applied to the weights of the added dense layer.
  • Finetuning Method:
    • Adjusting all the parameters of BERT along with the added classification head using backpropagation.
    • Using a lower learning rate than typically used in training from scratch.
  • PEFT (Parameter-Efficient Fine-Tuning):
    • LoRA: Incorporating low-rank matrices in Transformer layers without modifying pre-trained weights.
    • Adapters: Inserting small trainable layers between Transformer layers.
  • Token Sampling: Not typically applicable for BERT in standard fine-tuning scenarios.

Seq-Seq: RNN/Bidirectional LSTM:

  • Input: Sequential tokenized text data; each token can be represented as an embedding vector such as pre-trained (like GloVe or Word2Vec) or learned from scratch
  • Output: Similar to BERT, a probability distribution over classes for the classification task.
  • Layers Added:
    • Essentially, a BiLSTM consists of two LSTMs: one taking the input in a forward direction, and the other in a backward direction.
    • A fully connected layer for classification, with a dropout layer preceding it.
  • Loss Function: Categorical or binary cross-entropy, depending on the nature of the classification.
  • Regularization:
    • Dropout layers within the LSTM layers and/or before the fully connected layer.
    • L2 regularization can be applied to the LSTM and dense layers.
  • Finetuning Method:
    • Training the LSTM layers along with the classification head, adjusting the model to the specifics of the hate speech detection task.
    • Typically involves training from scratch if using a non-pre-trained LSTM model.
  • Token Sampling:
    • In cases of very long sequences, techniques like truncated backpropagation through time can be used to handle long dependencies without processing the entire sequence at once.

4) Evaluation Metrics: Offline Metrics: Accuracy, Precision, Recall, F1 Score, and possibly AUC-ROC for understanding model performance across various thresholds. BERT-Score, Movers-Score Online Metrics: If deployed in a live environment, track user feedback, model’s response time, and overall user engagement with the system.

Fake News

  • Classification of fake or real 1) Input and Output:

  • Input: Textual data of news articles.
  • Output: Classification result indicating whether the news is ‘real’ or ‘fake’.

2) Data:

  • Sources: Collect datasets comprising both genuine and fake news articles, including examples of neural fake news.
  • Volume and Variety: Ensure a large and diverse dataset to cover various news styles and content.

2) Data Preparation and Preprocessing:

  • Cleaning: Remove irrelevant information, normalize text (like lowercasing), and correct formatting issues.
  • Labeling: Accurately label each article as ‘real’ or ‘fake’ based on verified sources.

2) Tokenization and Embeddings:

  • Tokenization: Convert text into tokens. For deep learning models, consider using subword tokenization like BPE (Byte Pair Encoding) or WordPiece to handle out-of-vocabulary words.
  • Embeddings: Use pre-trained embeddings like GloVe or Word2Vec, or embeddings from models like BERT for richer contextual representation.

3) Features:

  • Textual Features: Extract features like n-grams, TF-IDF scores, sentiment scores, etc.
  • Contextual Features: If available, include author credibility, publishing source reliability, etc.
  • Handling Sparsity: Use dimensionality reduction techniques like PCA for high-dimensional sparse features.
  • Positional Bias: In sequence models (like LSTM), consider using positional encoding to maintain the order of words.

4) Model Selection:

  • BERT and Variants: For their ability to understand context and perform well on classification tasks.
    • BERT for Contextual Understanding:
    • Text Analysis with BERT: BERT’s role is to deeply analyze the structure and context of the news text. It can identify linguistic patterns and anomalies that are indicative of fake news.
    • Feature Extraction: BERT processes the news text and provides embeddings that can be used as features in identifying fake news.
  • GPT and RAG: Useful for generating responses based on retrieved information and input text.
    • RAG for Information Retrieval: RAG retrieves relevant information from a large corpus or database, providing factual data that can be used to verify the claims made in the news article.
    • GPT for Response Generation: GPT uses the retrieved information to generate an analysis or a verdict on the likelihood of the news being fake or real.
    • RAG:
      • New document -> embedding model -> vector db
      • Query -> embedding model -> FAISS -> document retrieval
  • LSTM/BiLSTM: For capturing long-term dependencies in text.
  • CNNs: For extracting local features from text.
  • Grover: AllenNLP

5) Fine-Tuning and Regularization:

  • Loss Function: Binary cross-entropy for binary classification tasks.
  • Optimization Algorithm: Adam or AdamW for efficient training.
  • Regularization Techniques: Implement dropout, early stopping, and possibly L1/L2 regularization.
  • Parameter-Efficient Fine-Tuning (PEFT): Techniques like Adapters or LoRA for efficient fine-tuning without extensive retraining.

5) Integrating Grover, BERT, and GPT with RAG:

  • Parallel Analysis: Each model analyzes the input news independently. BERT focuses on contextual embeddings, GPT with RAG on fact-checking and response generation, and Grover on identifying AI-generated text patterns.
  • Combined Decision-Making: The outputs from all three models are combined to make a final decision. This can be done through a voting system, a weighted average, or another decision-making algorithm that takes into account the strengths of each model.
  • Cross-Verification: The conclusions drawn by each model can be cross-verified with the others. For instance, if Grover identifies a piece as likely fake, but BERT and GPT with RAG suggest otherwise, the system might flag the article for further human review.

6) Token Sampling and Context Extension:

  • Token Sampling: Techniques like truncated backpropagation through time in sequence models to handle long texts.
  • Context Extension: Use mechanisms to handle longer context (like attention in Transformer models).

7) Evaluation Metrics:

  • Offline Metrics: Accuracy, Precision, Recall, F1 Score, and AUC-ROC.
  • Online Metrics: User feedback, real-time performance, adaptability to emerging fake news styles.

Named Entity Recognition

Designing a Named Entity Recognition (NER) system for medical documents involves creating a model that can accurately identify and classify medical terms and entities in text. Here’s a detailed plan for such a system:

1) Input and Output:

  • Input: Unstructured text data from medical documents, such as clinical notes, research papers, or patient records.
  • Output: Entities within the text identified and classified into categories like medication names, dosages, medical conditions, procedures, patient information, etc.

2) Examples of Data:

  • Clinical notes describing patient symptoms, diagnoses, and treatments.
  • Research papers with detailed descriptions of medical procedures, drugs, and clinical trials.
  • Patient records containing demographic information, medical history, and treatment plans.

3) Data Preparation and Preprocessing:

  • Data Cleaning: Remove irrelevant elements like headers, footers, or non-textual content.
  • Standardization: Convert text to a consistent format (e.g., uniform date formats, capitalization).
  • Anonymization: Ensure that sensitive patient information is anonymized for privacy.

4) Tokenization and Embeddings:

  • Tokenization: Use a tokenizer capable of handling medical terminology, possibly a custom tokenizer if standard ones (like spaCy’s tokenizer) are insufficient.
  • Embeddings: Employ domain-specific embeddings trained on medical texts (like BioBERT, a variant of BERT trained on biomedical literature) to capture the context and nuances of medical language.

5) Features:

  • Entity-Based Features: Include features specific to medical entities, such as drug names, symptoms, or procedure terms.
  • Contextual Features: Use embeddings that capture the context in which the entities occur.
  • Handling Sparsity: If using sparse representations like TF-IDF, apply dimensionality reduction techniques.
  • Positional Encoding: For models like Transformer-based ones, incorporate positional encodings to maintain the sequence of words.

6) Model Selection:

  • BERT and Variants (BioBERT, ClinicalBERT): Fine-tune these models on the medical NER task. They are effective in capturing contextual information in complex texts.
  • CRF (Conditional Random Fields): Useful for sequence modeling in NER. Can be combined with LSTM for enhanced performance.
  • BiLSTM with Attention Mechanism: A bidirectional LSTM to capture contextual dependencies, with an attention mechanism to focus on relevant parts of the text.

7) Fine-Tuning and Regularization:

  • Loss Function: Conditional Random Fields (CRF) loss is often used for sequence tagging tasks like NER.
  • Optimization Algorithm: Adam or AdamW, with learning rate scheduling.
  • Regularization: Implement dropout in LSTM layers and possibly weight decay in AdamW.
  • Parameter-Efficient Fine-Tuning (PEFT): Use techniques like adapters or layer-wise learning rate adjustments for efficient fine-tuning.

8) Token Sampling and Context Extension:

  • Token Sampling: Apply techniques like truncated backpropagation for dealing with long texts in RNN-based models.
  • Context Extension: Utilize attention mechanisms or Transformer-based models to handle longer contexts, essential in medical texts for capturing relevant information.

9) Evaluation Metrics:

  • Offline Metrics: Precision, Recall, and F1 score at the entity level.
  • Online Metrics (if deployed in a real-world setting): User feedback, system’s response time, and integration capability with existing medical record systems.

10) Implementation Steps:

  1. Data Annotation: Annotate a corpus of medical texts with the relevant entities.
  2. Model Training: Train the chosen model on this annotated dataset.
  3. Model Evaluation: Evaluate the model using the specified metrics on a held-out test set.
  4. Iterative Improvement: Continuously improve the model by retraining it with new data and refining based on feedback.

Sentiment Analysis

Designing a sentiment analysis system for movies involves creating a model that can interpret and classify the sentiment expressed in movie reviews. Here’s a detailed plan for such a system:

1) Input and Output:

  • Input: Textual data from movie reviews.

  • Output: Sentiment classification, typically as positive, negative, or neutral.

  • Examples of Data:

  • User-submitted reviews from movie databases like IMDb, Rotten Tomatoes, or Letterboxd.
  • Professional critic reviews from various online publications.

2) Data Preparation and Preprocessing:

  • Data Cleaning: Remove irrelevant information such as user details, timestamps, and non-textual elements from the reviews.
  • Standardization: Ensure the text is in a consistent format, like converting to lowercase and standardizing punctuation.
  • Handling Missing Values: If any review is missing significant text, consider removing or imputing it.

2) Tokenization and Embeddings:

  • Tokenization: Convert reviews into tokens using a tokenizer. For deep learning models, subword tokenization like BPE (Byte Pair Encoding) is beneficial.
  • Embeddings: Use pre-trained word embeddings like GloVe or Word2Vec. For more context-sensitive embeddings, models like BERT or its variants can be used.

2) Features:

  • Textual Features: Extract features like word n-grams, sentiment lexicons (e.g., AFINN, VADER), and bag-of-words models.
  • Handling Sparsity: For bag-of-words or TF-IDF representations, use dimensionality reduction techniques if necessary.
  • Positional Encoding: In Transformer-based models, positional encoding is inherent. For RNNs or LSTMs, consider the sequence in which words appear.

3) Model Selection:

  • RNN/LSTM: Good for capturing the order and context in text data. LSTMs are especially effective at handling long-range dependencies.
  • CNN: Can be used for extracting local features from text. Convolutional layers can capture n-gram interactions in the reviews.
  • BERT and Variants: Pre-trained models like BERT, which can be fine-tuned on the sentiment analysis task, are very effective due to their deep contextual understanding.
  • Transformers: The latest Transformer models (like GPT-2 or GPT-3) could be fine-tuned for sentiment analysis, providing high-quality contextual analysis.

4) Fine-Tuning and Regularization:

  • Loss Function: Use binary cross-entropy for binary sentiment classification or categorical cross-entropy for multi-class classification.
  • Optimization Algorithm: Adam or AdamW are generally effective, with scheduled learning rate decreases.
  • Regularization Techniques: Implement dropout, especially in RNN/LSTM models, to prevent overfitting.

5) Token Sampling and Context Extension:

  • Token Sampling: For lengthy reviews, consider using techniques like truncated backpropagation through time in RNNs or LSTMs.
  • Context Extension: Transformer-based models can handle longer contexts effectively.

6) Evaluation Metrics:

  • Offline Metrics: Precision, Recall, F1 Score, and Accuracy for classification performance.
  • Online Metrics: User engagement and feedback if deployed in a real-world application. Response time and system integration capabilities are also important.

7) Implementation Steps:

  1. Data Annotation: If not already labeled, the sentiment of each review needs to be annotated.
  2. Model Training: Train the chosen model on the annotated dataset.
  3. Model Evaluation: Use the specified metrics to evaluate model performance on a test set.
  4. Iterative Improvement: Refine the model based on evaluation results and potentially new data.

10) Additional Considerations:

  • Domain Adaptation: If using pre-trained models, adapt them to the specific language and style of movie reviews.
  • Bias Mitigation: Be aware of potential biases in the data, such as overrepresentation of certain types of movies or reviewers.
  • Continuous Learning: Continuously update the model with new reviews to keep up with evolving language and trends in movie reviewing.

This sentiment analysis system for movies would leverage NLP techniques and machine learning models to provide insights into public perception of films, aiding in both recommendation systems and market analysis.