80 citations · 380 across the 52 of their papers we have counts for
9 papers · 2 filters
Ontology Enrichment for Effective Fine-grained Entity Typing
Siru Ouyang, Jiaxin Huang, Pranav Pillai +3
Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional me…
Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder
Bowen Jin, Wentao Zhang, Yu Zhang +3
In real-world scenarios, texts in a graph are often linked by multiple semantic relations (e.g., papers in an academic graph are referenced by other publications, written by the sa…
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Yu Zhang, Bowen Jin, Xiusi Chen +4
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descript…
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Xiusi Chen, Yu Zhang, Jinliang Deng +2
Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available. Although existin…
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Kaixin Ma, Hao Cheng, Yu Zhang +3
The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated…
Patton: Language Model Pretraining on Text-Rich Networks
Bowen Jin, Wentao Zhang, Yu Zhang +4
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations…
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding
Yu Zhang, Hao Cheng, Zhihong Shen +3
Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery. Pre-trained language models (LMs) have shown…
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training
Yunyi Zhang, Minhao Jiang, Yu Meng +2
Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts. Most ex…
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Yu Zhang, Yunyi Zhang, Martin Michalski +3
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract dis…