Papers Entity Resolution

Anaphora and Coreference Resolution: A Review

Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research. This field possesses immense potential to improve the performan…

A High-Quality Gold Standard for Citation-based Tasks

Deep Learning for Entity Matching: A Design Space Exploration

Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions tha…

Entity Resolution and Federated Learning get a Federated Resolution

Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically p…

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is …

Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts

This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annot…

Complex and Holographic Embeddings of Knowledge Graphs: A Comparison

Embeddings of knowledge graphs have received significant attention due to their excellent performance for tasks like link prediction and entity resolution. In this short paper, we are providing a comparison of two state-…

Clustering with Noisy Queries

In this paper, we initiate a rigorous theoretical study of clustering with noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to recover the true clustering by asking minimum number of pairwise …

In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling

Entity resolution (ER) presents unique challenges for evaluation methodology. While crowdsourcing platforms acquire ground truth, sound approaches to sampling must drive labelling efforts. In ER, extreme class imbalance …

A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution

Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Due to inherent ambiguity of data representation and poor …

On Regularizing Rademacher Observation Losses

It has recently been shown that supervised learning linear classifiers with two of the most popular losses, the logistic and square loss, is equivalent to optimizing an equivalent loss over sufficient statistics about th…

Enforcing Relational Matching Dependencies with Datalog for Entity Resolution

Entity resolution (ER) is about identifying and merging records in a database that represent the same real-world entity. Matching dependencies (MDs) have been introduced and investigated as declarative rules that specify…

Flexible Models for Microclustering with Application to Entity Resolution

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pit…

An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets

Entity Resolution, also called record linkage or deduplication, refers to the process of identifying and merging duplicate versions of the same entity into a unified representation. The standard practice is to use a Rule…

Collective Entity Resolution with Multi-Focal Attention

Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation

Joint Event Detection and Entity Resolution: a Virtuous Cycle

Clustering web documents has numerous applications, such as aggregating news articles into meaningful events, detecting trends and hot topics on the Web, preserving diversity in search results, etc. At the same time, the…

Generic Statistical Relational Entity Resolution in Knowledge Graphs

Entity resolution, the problem of identifying the underlying entity of references found in data, has been researched for many decades in many communities. A common theme in this research has been the importance of incorp…

Clustering on the Edge: Learning Structure in Graphs

With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples. We show how learning cluster structure using edge features naturally and simultaneously de…

Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs

Several `edge-discovery' applications over graph-based data models are known to have worst-case quadratic time complexity in the nodes, even if the discovered edges are sparse. One example is the generic link discovery p…