Measuring syntactic complexity in spontaneous spoken Swedish

Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model

In the clinical linguistics of schizophrenia, syntactic complexity has received much attention. In this study, we address whether syntactic complexity deteriorates within the six months following the first episode of psychosis in those who develop a diagnosis of schizophrenia. We collected data from a cohort of twenty-six first-episode psychosis and 12 healthy control subjects using the Thought and Language Index interview in response to three pictures from the Thematic Apperception Test at first assessment and after six months (the time of consensus diagnosis). An automated labeling (part-of-speech tagging) for specific syntactic elements calculated large and granular syntactic complexity indices with a focus on clause complexity as a particular case from this spoken language data. Probabilistic reasoning leveraging the conditional independence properties of Bayes networks revealed that consensus diagnosis of schizophrenia predicted a decrease in nominal subjects per clause among individuals with first episode psychosis. From the entire sample, we estimate a 95.4 % probability that a 50 % decrease in mean nominal subjects per clause after six months is explained by the presence of first episode psychosis. Among those with psychosis, a 30 % decrease in this clause-complexity index after six months of experiencing the first episode predicted with 95 % probability a consensus diagnosis of schizophrenia, representing a conditional relationship between a longitudinal decrease in syntactic complexity and a diagnosis of schizophrenia. We conclude that an early drift towards linguistic disorganization/impoverishment of clause complexity-at the granular level of nominal subject per clause-is a distinctive feature of schizophrenia that decreases longitudinally, thus differentiating schizophrenia from other psychotic illnesses with shared phenomenology.

Linguistic Factors Influencing Speech Audiometric Assessment

In speech audiometric testing, hearing performance is typically measured by calculating the number of correct repetitions of a speech stimulus. We investigate to what extent the repetition accuracy of Dutch speech stimuli presented against a background noise is influenced by nonauditory processes. We show that variation in verbal repetition accuracy is partially explained by morpholexical and syntactic features of the target language. Verbs, prepositions, conjunctions, determiners, and pronouns yield significantly lower correct repetitions than nouns, adjectives, or adverbs. The reduced repetition performance for verbs and function words is probably best explained by the similarities in the perceptual nature of verbal morphology and function words in Dutch. For sentences, an overall negative effect of syntactic complexity on speech repetition accuracy was found. The lowest number of correct repetitions was obtained with passive sentences, reflecting the cognitive cost of processing a noncanonical sentence structure. Taken together, these findings may have important implications for the audiological practice. In combination with hearing loss, linguistic complexity may increase the cognitive demands to process sentences in noise, leading to suboptimal functional hearing in day-to-day listening situations. Using test sentences with varying degrees of syntactic complexity may therefore provide useful information to measure functional hearing benefits.

Making It Simplext

The way in which a text is written can be a barrier for many people. Automatic text simplification is a natural language processing technology that, when mature, could be used to produce texts that are adapted to the specific needs of particular users. Most research in the area of automatic text simplification has dealt with the English language. In this article, we present results from the Simplext project, which is dedicated to automatic text simplification for Spanish. We present a modular system with dedicated procedures for syntactic and lexical simplification that are grounded on the analysis of a corpus manually simplified for people with special needs. We carried out an automatic evaluation of the system’s output, taking into account the interaction between three different modules dedicated to different simplification aspects. One evaluation is based on readability metrics for Spanish and shows that the system is able to reduce the lexical and syntactic complexity of the texts. We also show, by means of a human evaluation, that sentence meaning is preserved in most cases. Our results, even if our work represents the first automatic text simplification system for Spanish that addresses different linguistic aspects, are comparable to the state of the art in English Automatic Text Simplification.