Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures (in Natural Language Processing and Text Mining)
Just as a sentence is far more than a mere concatenation of words, a text is far more than a mere concatenation of sentences. Texts contain pertinent information that co-refers across sentences and paragraphs; texts contain relations between phrases, clauses, and sentences that are often causally linked; and texts that depend on relating a series of chronological events contain temporal features that help the reader to build a coherent representation of the text. We refer to textual features such as these as cohesive elements, and they occur within paragraphs (locally), across paragraphs (globally), and in forms such as referential, causal, temporal, and structural. But cohesive elements, and by consequence cohesion, does not simply feature in a text as dialogues tend to feature in narratives, or as cartoons tend to feature in newspapers. That is, cohesion is not present or absent in a binary or optional sense. Instead, cohesion in text exists on a continuum of presence, which is sometimes indicative of the text-type in question and sometimes indicative of the audience for which the text was written. In this chapter, we discuss the nature and importance of cohesion; we demonstrate a computational tool that measures cohesion; and, most importantly, we demonstrate a novel approach to identifying text-types by incorporating contrasting rates of cohesion.
McCarthy, P. M., Briner, S., Rus, V., & MacNamara, D. S. (2007). Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures. In A. Kao & S. R. Poteet (Eds.), Natural language processing and text mining (pp. 107-122). London: Springer. doi:10.1007/978-1-84628-754-1_7