Papers Entity Resolution
“Entity Resolution” 태그가 달린 논문 214편 · 필터 해제
Fine-tuning Large Language Models for Entity Matching
Generative large language models (LLMs) are a promising alternative to pre-trained language models for entity matching due to their high zero-shot performance and ability to generalize to unseen entities. Existing resear…
Data IntegrationEntity ResolutionIn-Context LearningPrompt EngineeringEntity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap
Vertical Federated Learning (VFL) is a machine learning paradigm for learning from vertically partitioned data (i.e. features for each input are distributed across multiple "guest" clients and an aggregating "host" serve…
Entity AlignmentEntity ResolutionFederated LearningVertical Federated LearningLearning from Natural Language Explanations for Generalizable Entity Matching
Entity matching is the task of linking records from different sources that refer to the same real-world entity. Past work has primarily treated entity linking as a standard supervised learning problem. However, supervise…
Binary ClassificationDomain GeneralizationEntity LinkingEntity Resolution+1Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching
Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typically …
Entity ResolutionTowards Universal Dense Blocking for Entity Resolution
Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in block…
BlockingContrastive LearningEntity ResolutionHow to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
Entity resolution (record linkage, microclustering) systems are notoriously difficult to evaluate. Looking for a needle in a haystack, traditional evaluation methods use sophisticated, application-specific sampling schem…
Entity ResolutionMethods for Matching English Language Addresses
Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographical scope it refers to. The task of matching addresses happens everyday and…
Entity ResolutionNeural Locality Sensitive Hashing for Entity Blocking
Locality-sensitive hashing (LSH) is a fundamental algorithmic technique widely employed in large-scale data processing applications, such as nearest-neighbor search, entity resolution, and clustering. However, its applic…
BlockingEntity ResolutionSpatial Entity Resolution between Restaurant Locations and Transportation Destinations in Southeast Asia
As a tech company, Grab has expanded from transportation to food delivery, aiming to serve Southeast Asia with hyperlocalized applications. Information about places as transportation destinations can help to improve our …
Entity ResolutionOn Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach
Entity resolution, the task of identifying and merging records that refer to the same real-world entity, is crucial in sectors like e-commerce, healthcare, and law enforcement. Large Language Models (LLMs) introduce an i…
Entity ResolutionCost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
Entity resolution (ER) is an important data integration task with a wide spectrum of applications. The state-of-the-art solutions on ER rely on pre-trained language models (PLMs), which require fine-tuning on a lot of la…
Data IntegrationEntity ResolutionIn-Context LearningEntity Matching using Large Language Models
Entity matching is the task of deciding whether two entity descriptions refer to the same real-world entity. Entity matching is a central step in most data integration pipelines. Many state-of-the-art entity matching met…
Data IntegrationEntity ResolutionCost-Efficient Prompt Engineering for Unsupervised Entity Resolution
Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same underlying entity, with applications ranging from healthcare to e-commerce. Traditional ER solutions required co…
Entity ResolutionFeature EngineeringPrompt EngineeringGraph Representation Learning Towards Patents Network Analysis
Patent analysis has recently been recognized as a powerful technique for large companies worldwide to lend them insight into the age of competition among various industries. This technique is considered a shortcut for de…
Entity ResolutionGraph Representation LearningRepresentation LearningLabeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-…
Dataset GenerationEntity ResolutionPrivacy PreservingRevisiting Prompt Engineering via Declarative Crowdsourcing
Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits and recipes centered around so-called p…
Entity ResolutionImputationPrompt EngineeringNamed Entity Resolution in Personal Knowledge Graphs
Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of larg…
Entity ResolutionKnowledge GraphsA Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms
Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases. Numerous techniques have been developed to tackle ER challenges over the years, with r…
Entity ResolutionRecord Deduplication for Entity Distribution Modeling in ASR Transcripts
Voice digital assistants must keep up with trending search queries. We rely on a speech recognition model using contextual biasing with a rapidly updated set of entities, instead of frequent model retraining, to keep up …
Entity Resolutionspeech-recognitionSpeech RecognitionCombining Global and Local Merges in Logic-based Entity Resolution
In the recently proposed Lace framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identif…
Entity Resolution