Papers Entity Resolution

Principled Graph Matching Algorithms for Integrating Multiple Data Sources

This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. Entity resolution-the data integration problem of perfor…

Joint Bootstrapping of Corpus Annotations and Entity Types

Deriving Adjectival Scales from Continuous Space Word Representations

Graph-based Approaches for Organization Entity Resolution in MapReduce

Entity Linking for Tweets

NLP for uncertain data at scale

SOFTCARDINALITY: Hierarchical Text Overlap for Student Response Analysis

LASIGE: using Conditional Random Fields and ChEBI ontology

Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic

In many probabilistic first-order representation systems, inference is performed by "grounding"---i.e., mapping it to a propositional representation, and then performing propositional inference. With a large database of …

Graph-based Generalization Bounds for Learning Binary Relations

We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator. This problem has application in numerous areas of machine learning, such as ranking, entity r…

Annotation r\'ef\'erentielle du Corpus Arbor\'e de Paris 7 en entit\'es nomm\'ees (Referential named entity annotation of the Paris 7 French TreeBank) [in French]

Assessing Crowdsourcing Quality through Objective Tasks

The emergence of crowdsourcing as a commonly used approach to collect vast quantities of human assessments on a variety of tasks represents nothing less than a paradigm shift. This is particularly true in academic resear…

Aleda, a free large-scale entity database for French

Named entity recognition, which focuses on the identification of the span and type of named entity mentions in texts, has drawn the attention of the NLP community for a long time. However, many real-life applications nee…

A Three-Way Model for Collective Learning on Multi-Relational Data

Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other…