Augmenting WordNet-based inference with argument mapping

Abstract

WordNet is a useful resource for lexical inference in applications. Inference over predicates, however, often requires a change in argument positions, which is not specified in WordNet. We propose a novel framework for augmenting WordNet-based inferences over predicates with corresponding argument mappings. We further present a concrete implementation of this framework, which yields substantial improvement to WordNet-based inference.

Directional distributional similarity for lexical inference

“…It was previously successfully utilized for evaluating distributional similarity methods for recognizing lexical-syntactic entailment relations Szpektor and Dagan 2009). 4 This standard IE dataset contains thirty-three event types, such as Attack, Divorce and Law Suit, with all event mentions annotated in the corpus.…”

Learning Entailment Relations by Global Graph Structure Optimization

“…Thus, using WordNet directly to derive entailment rules between predicates is possible only for semantic relations such as hyponymy and synonymy, where arguments typically preserve their syntactic positions on both sides of the rule. Some knowledge bases try to overcome this difficulty: Nomlex (Macleod et al 1998) is a dictionary that provides the mapping of arguments between verbs and their nominalizations and has been utilized to derive predicative entailment rules (Meyers et al 2004;Szpektor and Dagan 2009). FrameNet (Baker, Fillmore, and Lowe 1998) is a lexicographic resource that is arranged around "frames": Each frame corresponds to an event and includes information on the predicates and arguments relevant for that specific event supplemented with annotated examples that specify argument positions.…”

“…Distributional similarity algorithms employ "the distributional hypothesis" (Harris 1954) and predict a semantic relation between two predicates by comparing the arguments with which they occur. Quite a few methods have been suggested (Lin and Pantel 2001;Szpektor et al 2004;Bhagat, Pantel, and Hovy 2007;Szpektor and Dagan 2008;Yates and Etzioni 2009;Schoenmackers et al 2010), which differ in terms of the specifics of the ways in which predicates are represented, the features that are extracted, and the function used to compute feature vector similarity. Next, we elaborate on some of the prominent methods.…”

“…To remedy that, Szpektor and Dagan (2008) suggested a directional distributional similarity measure. In their work, Szpektor and Dagan chose to represent predicates with unary templates, which are identical to binary templates, only they contain a predicate and a single argument, such as: X subj ← − − buys.…”

“…Because facts and knowledge are mostly expressed by propositions, such entailment rules are central to the TE task. This has led to active research on broad-scale acquisition of entailment rules for predicates (Lin and Pantel 2001;Sekine 2005;Szpektor and Dagan 2008;Yates and Etzioni 2009;Schoenmackers et al 2010).…”