Commonsense reasoning using path analysis on semantic networks
- Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.
Verfasserangaben: | Adam Mtarji |
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URN: | urn:nbn:de:kola-19767 |
Gutachter: | Steffen Staab |
Betreuer: | Claudia Schon, Steffen Staab |
Dokumentart: | Masterarbeit |
Sprache: | Englisch |
Datum der Fertigstellung: | 21.10.2019 |
Datum der Veröffentlichung: | 21.10.2019 |
Veröffentlichende Institution: | Universität Koblenz, Universitätsbibliothek |
Titel verleihende Institution: | Universität Koblenz, Fachbereich 4 |
Datum der Abschlussprüfung: | 21.10.2019 |
Datum der Freischaltung: | 21.10.2019 |
Seitenzahl: | ix, 38 |
Institute: | Fachbereich 4 / Institute for Web Science and Technologies |
BKL-Klassifikation: | 54 Informatik |
Lizenz (Deutsch): | Es gilt das deutsche Urheberrecht: § 53 UrhG |