Search Results for author: Michael Raymer

Found 5 papers, 1 papers with code

Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge

no code implementations27 Sep 2022 Cara Widmer, Md Kamruzzaman Sarker, Srikanth Nadella, Joshua Fiechter, Ion Juvina, Brandon Minnery, Pascal Hitzler, Joshua Schwartz, Michael Raymer

Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph.

Explainable Artificial Intelligence (XAI) Logical Reasoning

Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature

1 code implementation30 Mar 2022 Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

While transformer-based encoder-decoder models in a vanilla source document-to-summary setting have been extensively studied for abstractive summarization in different domains, their major limitations continue to be entity hallucination (a phenomenon where generated summaries constitute entities not related to or present in source article(s)) and factual inconsistency.

Abstractive Text Summarization Decoder +2

Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles

no code implementations3 Nov 2020 Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis.

Abstractive Text Summarization Document Summarization +8

Relating Input Concepts to Convolutional Neural Network Decisions

no code implementations21 Nov 2017 Ning Xie, Md. Kamruzzaman Sarker, Derek Doran, Pascal Hitzler, Michael Raymer

Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN's decision.

Decision Making Scene Recognition

Explaining Trained Neural Networks with Semantic Web Technologies: First Steps

no code implementations11 Oct 2017 Md. Kamruzzaman Sarker, Ning Xie, Derek Doran, Michael Raymer, Pascal Hitzler

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains.

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