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pro vyhledávání: '"Bizer, Christian"'
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 their ability to generalize to unseen entities. Existing research on using LLMs for ent
Externí odkaz:
http://arxiv.org/abs/2409.08185
The process of clearing areas, namely demining, starts by assessing and prioritizing potential hazardous areas (i.e., desk assessment) to go under thorough investigation of experts, who confirm the risk and proceed with the mines clearance operations
Externí odkaz:
http://arxiv.org/abs/2405.09444
Product offers on e-commerce websites often consist of a product title and a textual product description. In order to enable features such as faceted product search or to generate product comparison tables, it is necessary to extract structured attri
Externí odkaz:
http://arxiv.org/abs/2403.02130
E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. However, vendors often provide unstructured product descriptions, necess
Externí odkaz:
http://arxiv.org/abs/2310.12537
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 methods rely on pre-trained langu
Externí odkaz:
http://arxiv.org/abs/2310.11244
Structured product data in the form of attribute/value pairs is the foundation of many e-commerce applications such as faceted product search, product comparison, and product recommendation. Product offers often only contain textual descriptions of t
Externí odkaz:
http://arxiv.org/abs/2306.14921
Autor:
Korini, Keti, Bizer, Christian
Column type annotation is the task of annotating the columns of a relational table with the semantic type of the values contained in each column. Column type annotation is an important pre-processing step for data search and data integration in the c
Externí odkaz:
http://arxiv.org/abs/2306.00745
Autor:
Peeters, Ralph, Bizer, Christian
Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks of using these
Externí odkaz:
http://arxiv.org/abs/2305.03423
The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computationally intensive, leading to long runtimes. To reduce these runtimes,
Externí odkaz:
http://arxiv.org/abs/2303.03132
The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Cur
Externí odkaz:
http://arxiv.org/abs/2301.09521