Anti-Unification and Natural Language Processing

Abstract

Anti-unification is a well-known method to compute generalizations in logic. Given two objects, the goal of anti-unification is to reflect commonalities between these objects in the computed generalizations, and highlight differences between them.Anti-unification appears to be useful for various tasks in natural language processing. Semantic classification of sentences based on their syntactic parse trees, grounded language learning, semantic text similarity, insight grammar learning, metaphor modeling: This is an incomplete list of topics where generalization computation has been used in one form or another. The major anti-unification technique in these applications is the original method for first-order terms over fixed arity alphabets, introduced by Plotkin and Reynolds in 1970s, and some of its adaptations.The goal of this paper is to give a brief overview about existing linguistic applications of anti-unification, discuss a couple of powerful and flexible generalization computation algorithms developed recently, and argue about their potential use in natural language processing tasks.

One or Nothing: Anti-unification over the Simply-Typed Lambda Calculus

“…Anti-unification (generalization), first introduced in the 1970's by Plotkin and Reynolds [22,23], is a process that derives from a set of symbolic expressions a new symbolic expression possessing certain commonalities shared between its members. The concept has been apply to inductive theorem proving based on tree grammars [3], recursion scheme detection in functional programs [6], inductive synthesis of recursive functions [24], learning fixes from software code repositories [16,4], preventing bugs and misconfiguration [19], as well as uses within the fields of natural language processing and linguistics [17,2]. Applications, most related to our work are proof generalization [20] and higher-order term indexing [21], which use anti-unification over typed λ-terms and their extensions [5].…”