FlexER: Flexible Entity Resolution for Multiple Intents

• URL: http://arxiv.org/abs/2209.07569v1
• Date: Tue, 23 Aug 2022 15:52:52 GMT
• Title: FlexER: Flexible Entity Resolution for Multiple Intents
• Authors: Bar Genossar (1), Roee Shraga (2) and Avigdor Gal (1) ((1) Technion - Israel Institute of Technology, (2) Northeastern University)
• Abstract summary: We introduce the problem of multiple intents entity resolution (MIER), an extension to the universal (single intent) entity resolution task. We propose FlexER, utilizing contemporary solutions to universal entity resolution tasks to solve multiple intents entity resolution. A large-scale empirical evaluation introduces a new benchmark and, using also two well-known benchmarks, shows that FlexER effectively solves the MIER problem and outperforms the state-of-the-art for a universal entity resolution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, assuming the existence of a single interpretation of a real-world entity and focusing only on finding matched records, separating corresponding from non-corresponding ones, with respect to this single interpretation. However, in real-world scenarios, where entity resolution is part of a more general data project, downstream applications may have varying interpretations of real-world entities relating, for example, to various user needs. In what follows, we introduce the problem of multiple intents entity resolution (MIER), an extension to the universal (single intent) entity resolution task. As a solution, we propose FlexER, utilizing contemporary solutions to universal entity resolution tasks to solve multiple intents entity resolution. FlexER addresses the problem as a multi-label classification problem. It combines intent-based representations of tuple pairs using a multiplex graph representation that serves as an input to a graph neural network (GNN). FlexER learns intent representations and improves the outcome to multiple resolution problems. A large-scale empirical evaluation introduces a new benchmark and, using also two well-known benchmarks, shows that FlexER effectively solves the MIER problem and outperforms the state-of-the-art for a universal entity resolution.

Related papers

• Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition [3.5624857747396814]
  Recognizing flexible objects poses significant challenges due to their inherently diverse shapes and sizes, translucent attributes, ambiguous boundaries, and subtle inter-class differences. We propose the Flexible Vision Graph Neural Network (FViG) to optimize the self-saliency and thereby improve the discrimination of the representations for flexible objects.
  arXiv  Detail & Related papers  (2024-06-06T08:55:06Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• Modeling Entities as Semantic Points for Visual Information Extraction in the Wild [55.91783742370978]
  We propose an alternative approach to precisely and robustly extract key information from document images. We explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities. The proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-23T08:21:16Z)
• Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF [53.55504611255664]
  We propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. Our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities.
  arXiv  Detail & Related papers  (2022-11-09T09:23:56Z)
• Scalable Video Object Segmentation with Identification Mechanism [125.4229430216776]
  This paper explores the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object (VOS) We present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST) Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks.
  arXiv  Detail & Related papers  (2022-03-22T03:33:27Z)
• Graph Pattern Loss based Diversified Attention Network for Cross-Modal Retrieval [10.420129873840578]
  Cross-modal retrieval aims to enable flexible retrieval experience by combining multimedia data such as image, video, text, and audio. One core of unsupervised approaches is to dig the correlations among different object representations to complete satisfied retrieval performance without requiring expensive labels. We propose a Graph Pattern Loss based Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval.
  arXiv  Detail & Related papers  (2021-06-25T10:53:07Z)
• Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks [0.0]
  We propose a versatile, unified framework called HMill' for sample representation, model definition and training. We show an extension of the universal approximation theorem to the set of all functions realized by models implemented in the framework. We solve three different problems from the cybersecurity domain using the framework.
  arXiv  Detail & Related papers  (2021-04-19T21:32:44Z)
• Federated Unsupervised Representation Learning [56.715917111878106]
  We formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to learn a common representation model without supervision. FedCA is composed of two key modules: dictionary module to aggregate the representations of samples from each client and share with all clients for consistency of representation space and alignment module to align the representation of each client on a base model trained on a public data.
  arXiv  Detail & Related papers  (2020-10-18T13:28:30Z)
• Scale-Localized Abstract Reasoning [79.00011351374869]
  We consider the abstract relational reasoning task, which is commonly used as an intelligence test. Since some patterns have spatial rationales, while others are only semantic, we propose a multi-scale architecture that processes each query in multiple resolutions. We show that indeed different rules are solved by different resolutions and a combined multi-scale approach outperforms the existing state of the art in this task on all benchmarks by 5-54%.
  arXiv  Detail & Related papers  (2020-09-20T10:37:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.