107 citations · 184 across the 12 of their papers we have counts for
12 papers
ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision
Ming Zhong, Siru Ouyang, Minhao Jiang +4
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importanc…
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
Pengcheng Jiang, Shivam Agarwal, Bowen Jin +3
The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarg…
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing
Tanay Komarlu, Minhao Jiang, Xuan Wang +1
Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, is a basic but important task for knowledge extraction from un…
MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities
Priyanka Kargupta, Tanay Komarlu, Susik Yoon +2
Text classification is essential for organizing unstructured text. Traditional methods rely on human annotations or, more recently, a set of class seed words for supervision, which…
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
Yu Zhang, Yu Meng, Xuan Wang +2
Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater t…
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw…
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang +24
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and rel…
Minimally Supervised Categorization of Text with Metadata
Yu Zhang, Yu Meng, Jiaxin Huang +3
Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in…
Automatic Textual Evidence Mining in COVID-19 Literature
Xuan Wang, Weili Liu, Aabhas Chauhan +2
We created this EVIDENCEMINER system for automatic textual evidence mining in COVID-19 literature. EVIDENCEMINER is a web-based system that lets users query a natural language stat…
Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
Xuan Wang, Xiangchen Song, Bangzheng Li +2
We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13). This CORD-NER data…
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
Yu Zhang, Frank F. Xu, Sha Li +4
GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search…
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Xuan Wang, Yu Zhang, Xiang Ren +5
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 dise…