Abstract Natural Language Generation (NLG) is defined as the systematic approach for
producing human understandable natural language text based on non-textual data or from …

This article offers a survey of computational research on referring expression generation
(REG). It introduces the REG problem and describes early work in this area, discussing what …

Situational context is crucial for linguistic reference to visible objects, since the same
description can refer unambiguously to an object in one context but be ambiguous or …

We present NaturalOWL, a natural language generation system that produces texts
describing individuals or classes of OWL ontologies. Unlike simpler OWL verbalizers, which …

We introduce a novel algorithm for generating referring expressions, informed by human
and computer vision and designed to refer to visible objects. Our method separates absolute …

A substantial amount of recent work in natural language generation has focused on the
generation of ''one‐shot''referring expressions whose only aim is to identify a target referent …

We present a new approach to referring expression generation, casting it as a density
estimation problem where the goal is to learn distributions over logical expressions …

Natural language generation (NLG) is a major subfield of computational linguistics with a
long tradition as an application area of automated planning systems. While current …

Finding a logical formula that separates positive and negative examples given in the form of
labeled data items is fundamental in applications such as concept learning, reverse …

A referring expression in linguistics is any noun phrase identifying an object in a way that
will be useful to interlocutors. In the context of a query over a first order knowledge base K …