Probabilistic relaxed unification formalism and its application in question answering.

摘要

We explore the problem of graph unification of mismatched graphs. One of the assumptions of classical unification is that the knowledge base is complete and accurate, which seldom applies to the real world. We present relaxed unification as an alternative approach where the assumptions of classical unification are relaxed. Relaxed unification replaces the binary success or failure outcome of classical unification with a real number quantifying the correctness of the result. We provide a theoretical framework for relaxed unification by defining the relaxed unification formalism and present an algorithm for relaxed unification. We extend the formalism to probabilistic relaxed unification and devise an evaluation function that assigns correctness value to the result of the unification based on random walks in finite Markov chains.;Our approach is empirically validated through a series of cases drawn from real world questions and data collection. The validation cases substantiate that our system provides satisfactory results on the chosen dataset within the system limitations. They provide a detailed walkthrough of the system operation, demonstrate the granularity of the correctness function, present a method for incorporating a word similarity measure in the computation of the correctness function, and demonstrate the system limitations.;We present a modular framework for question answering and realize it in the implementation of a Relaxed Unification Question Answering system prototype. The system relies on the Jellyfish question answering system for question analysis and extraction of candidate answer. Retrieval of relevant documents is handled by the Apache Lucene retrieval engine. A semantic representation of the question and the candidate answers is produced using DELPH-IN tools and resource. Finally, the Relaxed Unification Module unifies the semantic representation of the candidate answers with that of the question, evaluates the correctness of the results, and ranks the final answers accordingly.