Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary

Jayant Krishnamurthy, Tom M. Mitchell


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 "Republican front-runner from Texas" whose semantics
cannot be represented using the Freebase schema. Our approach directly
converts a sentence's syntactic CCG parse into a logical 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 distribution over denotations for each textual
predicate. A training phase produces this probabilistic database using
a corpus of entity-linked text and probabilistic matrix factorization
with a novel ranking objective function. We evaluate our approach on a
compositional question answering task where it outperforms several
competitive baselines. We also compare our approach against manually
annotated Freebase queries, finding that our open predicate vocabulary
enables us to answer many questions that Freebase cannot.


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