Date of Original Version
Abstract or Description
In this work, we contribute a method that takes advantage of the powerful corpus of the Web data to automatically evaluate the truth of propositions that are stated as multiargument instantiated predicates, e.g., City In Country (Beijing,China). Our approach, OpenEval, automatically converts a given instantiated predicate into a Web search query, then extracts a corresponding set of features from the web pages returned. Initially, OpenEval trains a classifier on a list of predicates by using a set of seed positive examples for each predicate. Each such set furthermore provides negative examples for the other predicates. To evaluate a new query, OpenEval again converts the query into a corresponding set of features extracted from the Web. The extracted features are then used as input to the learned classifier. The classifier output is used to calculate the correctness probability of the input predicate. We experimentally show that OpenEval is significantly superior to the previous related techniques, in particular the Pointwise Mutual Information (PMI) and Never-Ending Language Learner (NELL).
Proceedings of the IJCAI Workshop on Learning by Reading and its Application, 2011, 46-49.