We present an algorithm for generating referring expressions in open domains . Existing algorithms work at the semantic level and assume the availability of a classification for attributes , which is only feasible for restricted domains . Our alternative works at the realisation level , relies on Word-Net synonym and antonym sets , and gives equivalent results on the examples cited in the literature and improved results for examples that prior approaches can not handle . We believe that ours is also the first algorithm that allows for the incremental incorporation of relations . We present a novel corpus-evaluation using referring expressions from the Penn Wall Street Journal Treebank . Referring expression generation has historically been treated as a part of the wider issue of generating text from an underlying semantic representation . The task has therefore traditionally been approached at the semantic level . Entities in the real world are logically represented ; for example ( ignoring quantifiers ) , a big brown dog might be represented as big1(x ) ∧ brown1(x ) ∧ dog1(x ) , where the predicates big1 , brown1 and dog1 represent different attributes of the variable ( entity ) x. The task of referring expression generation has traditionally been framed as the identification of the shortest logical description for the referent entity that differentiates it from all other entities in the discourse domain . For example , if there were a small brown dog ( small1(x ) ∧ brown1(x ) ∧ dog1(x ) ) in context , the minimal description for the big brown dog would be big1(x ) ∧ dog1(x)1 . This semantic framework makes it difficult to apply existing referring expression generation algorithms to the many regeneration tasks that are important today ; for example , summarisation , openended question answering and text simplification . Unlike in traditional generation , the starting point in these tasks is unrestricted text , rather than a semantic representation of a small domain . It is difficult to extract the required semantics from unrestricted text ( this task would require sense disambiguation , among other issues ) and even harder to construct a classification for the extracted predicates in the manner that existing approaches require ( cf . , § 2 ) . In this paper , we present an algorithm for generating referring expressions in open domains . We discuss the literature and detail the problems in applying existing approaches to reference generation to open domains in § 2 . We then present our approach in § 3 , contrasting it with existing approaches . We extend our approach to handle relations in § 3.3 and present a novel corpus-based evaluation on the Penn WSJ Treebank in § 4 . We have described an algorithm for generating referring expressions that can be used in any domain . Our algorithm selects attributes and relations that are distinctive in context . It does not rely on the availability of an adjective classification scheme and uses WordNet antonym and synonym lists instead . It is also , as far as we know , the first algorithm that allows for the incremental incorporation of relations and the first that handles nominals . In a novel evaluation , our algorithm successfully generates identical referring expressions to those in the Penn WSJ Treebank in over 80 % of cases . In future work , we plan to use this algorithm as part of a system for generation from a database of user opinions on products which has been automatically extracted from newsgroups and similar text . This is midway between regeneration and the classical task of generating from a knowledge base because , while the database itself provides structure , many of the field values are strings corresponding to phrases used in the original text . Thus , our lexicalised approach is directly applicable to this task .

Challenge: Existing semantic frameworks for referring expression generation algorithms work for only one domain and suffer tasks such as summarization because of unrestricted texts.
Approach: They propose an algorithm that selects distinctive attributes and relations coupled with WordNet without requiring an adjective classification scheme for open domain referring text generation.
Outcome: They evaluated the algorithm using a new corpus-evaluation from Penn WSJ Treebank and show that it can generate identical expressions in over 80% of cases.