… match” and “no-match” pairs (e1,e2) inC. We focus on machine learning (ML) based entity
match… , 43] and obviate the need for manually designing accurate matching functions [5, 24, 41]…

… We describe the components of design space in Section 3 and enumerate the concrete
instantiations in Section 4. Our experimental results are presented in Section 5 followed by …

… performing entity matching using deep learning. … neural network (NN) architectures used
extensively in the NLP literature. Those two models correspond to instances of the design space …

… the design space of different key modules wrt the deep learning-… the design space, so as
to derive the best EA performance. … , where a set of matching entity pairs are provided as the …

… deep learning in entity matching compared to traditional methods, and propose a taxonomy
of deep neural networks for entity matching… When we explore how the different methods do …

… is an example of a modern entity matching system based on pretrained language models. …
applying deep learning and pre-trained language models for solving the entity matching task…

… strategies in our design space of question batching and demonstration selection? We explore
the design space shown in … Doan, “Deep learning for blocking in entity matching: a design …

… , such as Named Entity Recognition (NER) tools, … ] designed an end-to-end EL system
used for downstream question answering systems. In summary, we consider it is worth exploring …

… deepmatcher python package [39], which provides built-in neural networks and utilities that
can train and apply state-of-the-art deep learning models for entity matching, to address EA. …

… introduced a design space for applying deep learning to EM. … EM, DeepER, DeepMatcher,
and Ditto formulate matching as a … We plan to further explore our design choices for injecting …