Global learning of textual entailment graphs

Two decades after the introduction of the World Wide Web, the world is experiencing an “information explosion”, that is, a rapid increase in the amount of available textual information (55). Consequently, the longstanding dream of developing systems that perform semantic inference over natural language text has become ever more pressing. Such systems are invaluable for many natural language understanding applications such as Question Answering, Information Extraction, Search, and Automatic Summarization. The main challenge in developing such methods is overcoming the problem of language variability: the fact that each target meaning can be expressed in natural language in a myriad of ways, which computer programs must recognize. Textual Entailment is a recent paradigm that reduces language understanding to the following task: given a text and a textual hypothesis, determine automatically whether a human reading the text would infer that the meaning of the hypothesis is true. This definition of textual entailment succinctly captures the type of inferences over text that we would like systems to perform. One of the crucial components in Textual Entailment systems is acquiring resources of “rules” that describe entailment relations between atomic units of language such as words and phrases. For example, the rule ‘cause an increase ⇒ affect ’ is useful since the text “salt causes an increase in blood pressure” textually entails “salt affects blood pressure”. The absence of such large and precise knowledge resources of entailment rules is currently the main stumbling block for semantic inference systems in general and Textual Entailment systems in particular. In this dissertation, we address this problem by developing methods for automatically learning entailment rules between verbal and predicative phrases. A substantial body of work on learning entailment rules has been carried out in the last decade. However, most prior work considered each rule in isolation, although entailment rules strongly interact with one another. In this dissertation, we propose models that improve rule learning by exploiting such interactions, most notably the fact that entailment rules are transitive, i.e., if ‘A ⇒ B ’ and ‘B ⇒ C ’, then ‘A ⇒ C ’. We suggest the problem of learning entailment graphs, where graph nodes correspond to predicative phrases and edges represent entailment rules. Given the graph nodes, our goal is to find the optimal set of entailment edges respecting the constraint of transitivity. We show that this optimization problem is NP-hard and suggest to find an exact solution by formulating the problem as an Integer Linear Program (ILP) and applying a standard ILP solver. We empirically test our method over a set of manually-annotated entailment graphs and demonstrate that learning graphs that respect the constraint of transitivity significantly improves the quality of learned rules. Since ILP solvers do not scale well, it is imperative to develop methods for learning large entailment graphs that contain many entailment rules. A major contribution of this dissertation is in suggesting several such methods. Our first insight is that entailment graphs tend to be sparse, that is, most predicates do not entail one another. We show that sparseness allows us to decompose large graphs into several smaller components, and to exactly solve each component independently of the others. We empirically demonstrate that this approach substantially improves scalability over a large set of entailment graphs. A second improvement in scalability is achieved by identifying a novel structural property of entailment graphs, namely, that they tend to have a “tree-like” structure. We formally define this notion and prove that by assuming that entailment graphs are “tree-like”, an efficient polynomial approximation algorithm for learning entailment graphs can be devised, which empirically scales to graphs containing tens of thousands of nodes. We apply our methods over an open domain web-scale data set and generate new knowledge resources containing hundreds of thousands and even millions of predicative entailment rules. We show that our resources compare favorably to current state-of-the-art knowledge-bases in several evaluations, and publicly release these resources for the benefit of the scientific community. Last, we present a novel application for text exploration that is based on learning entailment graphs. We propose that entailment graphs can aid text exploration by allowing users to navigate from one document to another based on entailment relations between propositions in the documents. As a case study, we implement a text exploration system in the health-care domain. To conclude, this dissertation demonstrates that by exploiting the structural properties of entailment graphs one can achieve more precise and scalable methods for entailment rule learning. This opens the door for further research on the structure of entailment graphs and on the dependencies between textual entailment and other semantic relations. We believe that taking this global view on the problem of learning semantic relations can lead to algorithms that learn large and coherent knowledge resources, which are so essential for progress in the field of semantic inference.