We present a probabilistic model for extracting and storing information from WordNet and the British National Corpus. We map the data into a directed probabilistic graph that can be used to compute the conditional probability between a pair of words from the English language. For example, the graph can be used to deduce that there is a 10% probability that someone who is interested in dogs is also interested in the word “canine”. We propose three ways for computing this probability, where the best results are achieved when performing multiple random walks in the graph. Unlike existing approaches that only process the structured data in WordNet, we process all available information, including natural language descriptions. The available evidence is expressed as simple Horn clauses with probabilities. It is then aggregated using a Markov Logic Network model to create the probabilistic graph. We experimentally validate the quality of the data on five different benchmarks that contain collections of pairs of words and their semantic similarity as determined by humans. In the experimental section, we show that our random walk algorithm with logarithmic distance metric produces higher correlation with the results of the human judgment on three of the five benchmarks and better overall average correlation than the current state-of-the-art algorithms.


Computer Sciences

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URL: https://digitalcommons.calpoly.edu/csse_fac/270