Learning a compositional semantics for Freebase with an open predicate vocabulary

We present an approach to learning a model-theoretic semantics for natural language tied to Freebase. Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases such as “Re-publican front-runner from Texas ” whose se-mantics cannot be represented using the Free-base schema. Our approach directly converts a sentence’s syntactic CCG parse into a log-ical form containing predicates derived from the words in the sentence, assigning each word a consistent semantics across sentences. This logical form is evaluated against a learned probabilistic database that defines a distribu-tion over denotations for each textual pred-icate. A training phase produces this prob-abilistic database using a corpus of entity-linked text and probabilistic matrix factoriza-tion with a novel ranking objective function. We evaluate our approach on a compositional question answering task where it outperforms several competitive baselines. We also com-pare our approach against manually annotated Freebase queries, finding that our open pred-icate vocabulary enables us to answer many questions that Freebase cannot.