Clayton T. Morrison — accepted AI-conference papers

2024

When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context

Enrique Noriega-Atala, Robert Vacareanu, Salena Torres Ashton, Adarsh Pyarelal, Clayton T Morrison, Mihai Surdeanu

EMNLP 2024finding

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our app…

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2023

Information Extraction from Legal Wills: How Well Does GPT-4 Do?

Alice Saebom Kwak, Cheonkam Jeong, Gaetano Vincent Forte, Derek Bambauer, Clayton T Morrison, Mihai Surdeanu

EMNLP 2023short findings

This work presents a manually annotated dataset for Information Extraction (IE) from legal wills, and relevant in-context learning experiments on the dataset. The dataset consists of entities, binary relations between the entities (e.g., relations between testator and beneficiary), and n-ary events…

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2017

An Infinite Hidden Markov Model With Similarity-Biased Transitions

Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison

ICML 2017poster

We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between “nearby” states. This is accomplished by defining a similarity function on the state space and scaling transition…

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