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Identifying the Discourse Function of News Article Paragraphs
- W. V. Yarlott, Cristina Cornelio, Tian Gao, Mark A. Finlayson
- Computer ScienceEventStory@Coling
- 1 August 2018
This work applied the hierarchical theory of news discourse developed by van Dijk to examine how paragraphs operate as units of discourse structure within news articles to generate a gold-standard, adjudicated corpus of 50 documents for document-level discourse annotation based on the ACE Phase 2 corpus.
Dynamic and Probabilistic CP-nets
- Cristina Cornelio
In this paper we present a two-fold generalization of conditional preference networks (CP-nets) that incorporates uncertainty. CPnets are a formal tool to model qualitative conditional preference…
Updates and Uncertainty in CP-Nets
- Cristina Cornelio, J. Goldsmith, Nicholas Mattei, F. Rossi, K. Venable
- Mathematics, Computer ScienceAustralasian Conference on Artificial…
- 1 December 2013
This paper presents and study a generalization of CP-nets which supports changes and allows for encoding uncertainty, expressed in probabilistic terms, over the structure of the dependency links and over the individual preference relations.
Reasoning with PCP-nets in a Multi-Agent Context
- Cristina Cornelio, Umberto Grandi, J. Goldsmith, Nicholas Mattei, F. Rossi, K. Venable
- Computer ScienceAAMAS
- 4 May 2015
This paper uses PCP-nets in a multi-agent context to compactly represent a collection of CP-nets, thus using probabilistic uncertainty to reconcile possibly conflicting qualitative preferences expressed by a group of agents.
Symbolic Regression using Mixed-Integer Nonlinear Optimization
Tyler Josephson and Lior Horesh gratefully acknowledge the support of the Institute for Mathematics and its Applications (IMA), where a part of this work was initiated.
Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning
A semantic parsing and reasoning-based Neuro-Symbolic Question Answering system that achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0 and integrates multiple, reusable modules that are trained specifically for their individual tasks and do not require end-to-end training data.
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
This work proposes a novel approach for embedding logical formulae that is designed to overcome the representational limitations of prior approaches and achieves state-of-the-art performance on both premise selection and proof step classification.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Neuro-Symbolic Question Answering (NSQA) is proposed, a modular KBQA system that leverages Abstract Meaning Representation (AMR) parses for task-independent question understanding and achieves state-of-the-art performance on two prominentKBQA datasets based on DBpedia.
Multi-agent soft constraint aggregation via sequential voting: theoretical and experimental results
- Cristina Cornelio, M. S. Pini, F. Rossi, K. Venable
- Computer ScienceAutonomous Agents and Multi-Agent Systems
- 19 January 2019
The experimental study shows that the proposed sequential procedure yields a considerable saving in time with respect to a non-sequential approach, while the winners satisfy the agents just as well, independently of the variable ordering, and of the presence of coalitions of agents.
Voting with CP-nets using a Probabilistic Preference Structure
Probabilistic conditional preference networks (PCP-nets) provide a compact representation of a probability distribution over a collection of CP-nets. In this paper we view a PCP-net as the result of…