Jingbo Shang

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

2018 · arXiv (Cornell University)

Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free …

Learning Named Entity Tagger using Domain-Specific Dictionary

2018 · arXiv (Cornell University)

Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing …

SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank

2023

Deep neural classifiers trained with cross-entropy loss (CE loss) often suffer from poor calibration, necessitating the task of out-of-distribution (OOD) detection. Traditional supervised OOD detection methods require expensive manual annotation of in-distribution and OOD samples. …

Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs

2024 · arXiv (Cornell University)

Controlling the attribute intensity of text generation is crucial across scenarios (e.g., writing conciseness, chatting emotion, and explanation clarity). The remarkable capabilities of large language models (LLMs) have revolutionized text generation, prompting us to explore …

Empower Sequence Labeling with Task-Aware Neural Language Model

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Linguistic sequence labeling is a general approach encompassing a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted …

Empower Sequence Labeling with Task-Aware Neural Language Model

2017 · arXiv (Cornell University)

Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models …

Text Is All You Need: Learning Language Representations for Sequential Recommendation

2023

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still …