The bit in the middle and why it's important: a computational analysis of the linguistic features of body paragraphs.

This study examines the relationship between the linguistic characteristics of body paragraphs of student essays and the total number of paragraphs in the essays. Results indicate a significant relationship between the total number of paragraphs and a variety of linguistic characteristics known to affect student essay scores. These linguistic characteristics (e.

MTLD, vocd-D, and HD-D: a validation study of sophisticated approaches to lexical diversity assessment.

The main purpose of this study was to examine the validity of the approach to lexical diversity assessment known as the measure of textual lexical diversity (MTLD). The index for this approach is calculated as the mean length of word strings that maintain a criterion level of lexical variation. To validate the MTLD approach, we compared it against the performances of the primary competing indices in the field, which include vocd-D, TTR, Maas, Yule's K, and an HD-D index derived directly from the hypergeometric distribution function.

The components of paraphrase evaluations.

Two sentences are paraphrases if their meanings are equivalent but their words and syntax are different. Paraphrasing can be used to aid comprehension, stimulate prior knowledge, and assist in writing-skills development. As such, paraphrasing is a feature of fields as diverse as discourse psychology, composition, and computer science.

Identifying topic sentencehood.

Four experiments were conducted to assess two models of topic sentencehood identification: the derived model and the free model. According to the derived model, topic sentences are identified in the context of the paragraph and in terms of how well each sentence in the paragraph captures the paragraph's theme. In contrast, according to the free model, topic sentences can be identified on the basis of sentential features without reference to other sentences in the paragraph (i.

Using temporal cohesion to predict temporal coherence in narrative and expository texts.

We investigated the linguistic features of temporal cohesion that distinguish variations in temporal coherence. In an analysis of 150 texts, experts rated temporal coherence on three continuous scale measures designed to capture unique representations of time. Coh-Metrix, a computational tool that assesses textual cohesion, correctly predicted the human ratings with five features of temporal cohesion.