462 citations · 1.5k across the 58 of their papers we have counts for
16 papers · 1 filter
SCStory: Self-supervised and Continual Online Story Discovery
Susik Yoon, Yu Meng, Dongha Lee +1
We present a framework SCStory for online story discovery, that helps people digest rapidly published news article streams in real-time without human annotations. To organize news…
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Xinyang Zhang, Chenwei Zhang, Xian Li +4
Automatic extraction of product attributes from their textual descriptions is essential for online shopper experience. One inherent challenge of this task is the emerging nature of…
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Jiaming Shen, Zeqiu Wu, Dongming Lei +5
Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand.…
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
Jiaming Shen, Zeqiu Wu, Dongming Lei +3
Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task i…
Task-Guided Pair Embedding in Heterogeneous Network
Chanyoung Park, Donghyun Kim, Qi Zhu +2
Many real-world tasks solved by heterogeneous network embedding methods can be cast as modeling the likelihood of pairwise relationship between two nodes. For example, the goal of…
Weakly-Supervised Hierarchical Text Classification
Yu Meng, Jiaming Shen, Chao Zhang +1
Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models a…
Mining Entity Synonyms with Efficient Neural Set Generation
Jiaming Shen, Ruiliang Lyu, Xiang Ren +3
Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on…
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
Yuning Mao, Xiang Ren, Jiaming Shen +2
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e.,…
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
Meng Qu, Xiang Ren, Yu Zhang +1
Extracting relations from text corpora is an important task in text mining. It becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, uti…
Indirect Supervision for Relation Extraction using Question-Answer Pairs
Zeqiu Wu, Xiang Ren, Frank F. Xu +2
Automatic relation extraction (RE) for types of interest is of great importance for interpreting massive text corpora in an efficient manner. Traditional RE models have heavily rel…
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
Liyuan Liu, Xiang Ren, Qi Zhu +4
Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-co…
Automatic Synonym Discovery with Knowledge Bases
Meng Qu, Xiang Ren, Jiawei Han
Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g.…
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
Meng Jiang, Jingbo Shang, Taylor Cassidy +4
Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency…
Automated Phrase Mining from Massive Text Corpora
Jingbo Shang, Jialu Liu, Meng Jiang +3
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus. Phrase mining is important in various tasks such as informati…
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Xiang Ren, Zeqiu Wu, Wenqi He +5
Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have rel…
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Xiang Ren, Wenqi He, Meng Qu +3
Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, t…