• Publications
  • Influence
ArnetMiner: extraction and mining of academic social networks
TLDR
This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Expand
  • 1,398
  • 161
  • PDF
Modeling Relations and Their Mentions without Labeled Text
TLDR
We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decisionwhether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. Expand
  • 809
  • 160
  • PDF
Relation Extraction with Matrix Factorization and Universal Schemas
TLDR
We present a new approach to implicature with universal schemas and show that it outperforms state-of-the-Art distant supervision. Expand
  • 551
  • 71
  • PDF
Efficient methods for topic model inference on streaming document collections
TLDR
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Expand
  • 392
  • 45
  • PDF
Structured Relation Discovery using Generative Models
TLDR
We propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them. Expand
  • 129
  • 16
  • PDF
Multi-topic Based Query-Oriented Summarization
TLDR
Query-oriented summarization (QS) aims at extracting an informative summary from a document collection for a given query. Expand
  • 107
  • 11
  • PDF
A Combination Approach to Web User Profiling
TLDR
In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the “semantic”-based user profile from the Web. Expand
  • 131
  • 9
  • PDF
Collective Cross-Document Relation Extraction Without Labelled Data
TLDR
We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. Expand
  • 137
  • 9
  • PDF
Universal schema for entity type prediction
TLDR
We propose a universal schema approach to fine-grained entity type prediction by learning latent vector embeddings from probabilistic matrix factorization, thus avoiding the need for hand-labeled data. Expand
  • 44
  • 5
  • PDF
Extraction and mining of an academic social network
TLDR
This paper addresses several key issues in extraction and mining of an academic social network: 1) extraction of a researcher social network from the existing Web; 2) integration of the publications from existing digital libraries; 3) expertise search on a given topic; 4) association search between researchers. Expand
  • 59
  • 4
  • PDF