Research map / Label-efficient and robust learning / Recognition and long-tail

Enriching Word Embeddings with a Regressor Instead of Labeled Corpora

AAAI 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Loss design and classifier theory

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• A Simple but Tough-to-Beat Baseline for Sentence Embeddings (ICLR 2017 · similarity 0.90)
• No Training Required: Exploring Random Encoders for Sentence Classification (ICLR 2019 · similarity 0.88)
• Learning Semantic Representations for Novel Words: Leveraging Both Form and Context (AAAI 2019 · similarity 0.88)
• Learning Sentiment-Specific Word Embedding via Global Sentiment Representation (AAAI 2018 · similarity 0.88)
• Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe (NeurIPS 2024 · similarity 0.86)
• Attention with Trained Embeddings Provably Selects Important Tokens (NeurIPS 2025 · similarity 0.86)