We use Generalized Entity Matching (GEM) to satisfy these practical requirements and present an end-to-end pipeline, Machop, as the solution. Machop allows end users to define new matching tasks from scratch and apply them to new domains in a step-by-step manner. Machop casts the GEM problem as sequence pair classification so as to utilize the language understanding capability of Transformers-based language models (LMs) such as BERT.
it is important to develop a framework to explain the results of DL-based EM models. We developed Minun, a model-agnostic framework to provide local explanations for black-box EM models. Minun employs counterfactual examples of entity pairs as the explanation.
In this blog post, we take one step beyond the current scope of opinion summarization and propose CoCoSum, a framework which aims to generate contrastive and common summaries by comparing multiple entities. This framework consists of two base summarization models that jointly generate contrastive and common summaries.
Supporting Humans in the Information Extraction Loop: An In-depth Study of the Practices, Limitations, and Opportunities
Information extraction (IE) is often the crucial first step in text-analysis tasks such as entity matching, knowledge-base population, and text summarization. Typically, data science workflows