Enhancing Hyperspace Analogue to Language (HAL) Representations via Attention-Based Pooling for Text Classification

1. 1Construct global word co-occurrence matrices using the HAL model
2. 2Apply attention-based pooling to aggregate token representations into sentence-level embeddings
3. 3Assign weights to tokens based on contextual salience
4. 4Evaluate the performance of the enhanced HAL representations on text classification tasks

NLP researchers and AI engineers can benefit from this approach to improve text classification accuracy, as it allows for more nuanced sentence-level embeddings

💡 Attention-based pooling can reduce information loss in HAL representations by assigning more weight to contextually salient words

💡 Boost text classification with attention-based pooling for HAL representations