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UTexas: Probabilistic Hypothesis Generation For TAC SM-KBP Task 3.pdf

TAC2018.UTexas.proceedings.pdf
Preview of UTexas: Probabilistic Hypothesis Generation for TAC SM-KBP Task 3
🔗 Source: tac.nist.gov
📊 Size: 106 KB
📄 Pages: 5 pages
⬇️ Downloads: 937

Summary

The UTexas system for TAC SM-KBP task 3, "Probabilistic generation of coherent hypotheses," addresses the challenge of constructing internally consistent narratives (hypotheses) from an incompatible knowledge base, treating it as a one-class clustering task combined with inference. The system uses probabilistic programming to generate hypotheses by selecting connected and compatible subgraphs.

Input: A knowledge graph in AIDA Interchange Format (AIF), containing entities, events, relations, and coreference statements with confidence weights.

Task: Generate hypotheses based on Statements of Information Need, which specify entry points (entities, events, or relations), frames (different perspectives), and the number of hops to explore.

System Details:

1. Probabilistic hypothesis generation: The system generates hypotheses through a probabilistic generative process, starting from entry points and using a particle filter to sample statements based on their coherence with the current cluster.
2. Statement sampling: The process involves two stages: satisfying frame edges and adding statements not specified in the frame, using a fuzzy boundary on coherence determined by a gamma distribution.
3. Coreference: The system models coreference grouping as probabilistic unification, where an entity, event, or relation unifies with the prototype member of a coreference group with a probability based on the group's membership.

Output: The system returns the n hypotheses with the highest probability.

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Description

Generates coherent hypotheses probabilistically.

Technical Information

  • File Format: PDF
  • File Size: 106 KB
  • Pages: 5
  • Language: EN
  • Total Downloads: 937
  • Last Updated: 3 weeks ago

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