Finding a language fingerprint: Using the Hyperspace Analogue to Language (HAL) model to detect individual and population linguistic patterns

Contextual co-occurrence theories of language learning posit that distributional information is used in the formation and representation of semantic memory. Because learning environments are diverse, differences in semantic memory are likely to occur for individuals with disparate learning histories. The overarching goal of this research was to develop procedures for detecting unique linguistic patterns among groups of individuals that reflect these underlying semantic representations and, thus, could be used as a means of linguistic differentiation. The Hyperspace Analogue to Language (HAL) model of semantic memory was employed to create high-dimensional representations of text samples using co-occurrence information. These representations formed 'language fingerprints' from which both descriptive and diagnostic information was obtained through the use of HAL metrics that examined different aspects of the matrix representations. Six experiments assessed this methodology's discriminatory and descriptive powers using texts authored by participants with different language backgrounds. Spanish-English bilinguals, Chinese-English bilinguals, and English monolinguals wrote a 500 to 750-word letter from one of three perspectives: a bomb threat, a ransom note, or a letter asking for a charity donation. Semantic representations were developed for each individual's text in addition to three population representations that combined text from the members of the same language group. Aspects of the individual representations were compared to those of the group representations to ascertain which group pattern it most resembled. Results revealed that the diagnostic efficacy of the methodology interacted with language group and metric. However, combining metrics eliminated this interaction such that all but one comparison showed that intra-group correlations were stronger than the inter-group correlations. This suggested that (1) co-occurrence information is a meaningful source of information for differentiation, (2) the current methodology was successful in detecting linguistic patterns with which to determine group membership, and (3) the use of high-dimensional models provides illuminating and useful ways of understanding variation in human behavior. As such, this research represents a novel way of examining individual/population differences through the use of a model that provides a cognitive theory of the development of these differences, unlike the majority of linguistic profiling and differentiating techniques in current use.