Supported Tasks

PyABSA is a comprehensive framework that supports a wide range of natural language processing tasks, with a primary focus on aspect-based sentiment analysis. This section provides an overview of all supported tasks and their applications.

Core ABSA Tasks

Aspect-Based Sentiment Classification (ABSC)

What it does: Determines the sentiment polarity (positive, negative, neutral) towards specific aspects mentioned in text.

Use cases:

  • Product review analysis

  • Customer feedback processing

  • Social media sentiment monitoring

Example: “The [B-ASP]food[E-ASP] was excellent, but the [B-ASP]service[E-ASP] was slow.”

  • Aspect: “food” → Sentiment: Positive

  • Aspect: “service” → Sentiment: Negative

→ See ABSC Examples

Aspect Term Extraction (ATE)

What it does: Automatically identifies and extracts aspect terms from text without requiring pre-marked aspects.

Use cases:

  • Automated opinion mining

  • Market research

  • Content analysis

Example: “The pizza was delicious and the atmosphere was cozy.”

  • Extracted aspects: “pizza”, “atmosphere”

→ See ATE Examples

Aspect Sentiment Triplet Extraction (ASTE)

What it does: Simultaneously extracts aspect terms, opinion terms, and their sentiment relationships in a single model.

Use cases:

  • Comprehensive opinion analysis

  • Fine-grained sentiment understanding

  • Advanced text analytics

Example: “The pizza was delicious but the service was terrible.”

  • Triplet 1: (pizza, delicious, positive)

  • Triplet 2: (service, terrible, negative)

→ See ASTE Examples

Extended Tasks

Text Classification

What it does: General-purpose text classification for various domains and use cases.

Use cases:

  • Document categorization

  • Topic classification

  • Intent detection

→ See Text Classification Examples

Textual Adversarial Defense

What it does: Protects models from adversarial attacks that attempt to fool sentiment classifiers with subtle text modifications.

Use cases:

  • Robust model deployment

  • Security-critical applications

  • Model reliability enhancement

→ See Adversarial Defense Examples

Specialized Domains

RNA Sequence Classification

What it does: Classifies RNA sequences for biological research applications.

Use cases:

  • Bioinformatics research

  • Genomic analysis

  • Functional annotation

→ See RNA Classification Examples

RNA Sequence Regression

What it does: Predicts continuous values for RNA sequences, such as expression levels or binding affinities.

Use cases:

  • Gene expression prediction

  • Protein-RNA interaction analysis

  • Regulatory element scoring

→ See RNA Regression Examples

Getting Started with Tasks

Each task in PyABSA follows a similar workflow:

  1. Load or configure a model using the task-specific API

  2. Prepare your data in the required format

  3. Train (optional) or load a pre-trained model

  4. Run inference on your data

  5. Evaluate results and visualize metrics

For detailed examples and code snippets, click on the links above or refer to the tutorials section in this documentation.

Model Architecture Support

PyABSA supports various state-of-the-art model architectures for each task:

  • Transformer-based models: BERT, RoBERTa, DeBERTa, and more

  • Task-specific architectures: FAST-LSA, LCF-BERT, LCFS-BERT

  • Custom models: Easy integration of your own architectures

Performance and Benchmarks

PyABSA models are evaluated on standard benchmark datasets and consistently achieve competitive or state-of-the-art performance. For detailed benchmark results, please refer to our research papers and the performance metrics in individual task examples.