Intrinsic Natural Language Generation (NLG) Performance Measure

From GM-RKB
(Redirected from Intrinsic NLG Measure)
Jump to navigation Jump to search

An Intrinsic Natural Language Generation (NLG) Performance Measure is a NLG measure that is an intrinsic measure (it evaluates generated text quality independently of its impact on real-world systems).



References

2011

  • (Crossley & McNamara, 2011) ⇒ Scott A. Crossley, and Danielle S. McNamara. (2011). “Understanding Expert Ratings of Essay Quality: Coh-Metrix Analyses of First and Second Language Writing.” International Journal of Continuing Engineering Education and Life Long Learning, 21(2-3).
    • ABSTRACT: This article reviews recent studies in which human judgements of essay quality are assessed using Coh-Metrix, an automated text analysis tool. The goal of these studies is to better understand the relationship between linguistic features of essays and human judgements of writing quality. Coh-Metrix reports on a wide range of linguistic features, affording analyses of writing at various levels of text structure, including surface, text-base, and situation model levels. Recent studies have examined linguistic features of essay quality related to co-reference, connectives, syntactic complexity, lexical diversity, spatiality, temporality, and lexical characteristics. These studies have analysed essays written by both first language and second language writers. The results support the notion that human judgements of essay quality are best predicted by linguistic indices that correlate with measures of language sophistication such as lexical diversity, word frequency, and syntactic complexity. In contrast, human judgements of essay quality are not strongly predicted by linguistic indices related to cohesion. Overall, the studies portray high quality writing as containing more complex language that may not facilitate text comprehension.