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Using the Google Ngram Corpora for six different languages (including two varieties of English), a large-scale time series analysis is conducted. It is demonstrated that diachronic changes of the parameters of the Zipf–Mandelbrot law (and the parameter of the Zipf law, all estimated by maximum likelihood) can be used to quantify and visualize important aspects of linguistic change (as represented in the Google Ngram Corpora). The analysis also reveals that there are important cross-linguistic differences. It is argued that the Zipf–Mandelbrot parameters can be used as a first indicator of diachronic linguistic change, but more thorough analyses should make use of the full spectrum of different lexical, syntactical and stylometric measures to fully understand the factors that actually drive those changes.
Two very reliable influences on eye fixation durations in reading are word frequency, as measured by corpus counts, and word predictability, as measured by cloze norming. Several studies have reported strictly additive effects of these 2 variables. Predictability also reliably influences the amplitude of the N400 component in event-related potential studies. However, previous research suggests that while frequency affects the N400 in single-word tasks, it may have little or no effect on the N400 when a word is presented with a preceding sentence context. The present study assessed this apparent dissociation between the results from the 2 methods using a coregistration paradigm in which the frequency and predictability of a target word were manipulated while readers’ eye movements and electroencephalograms were simultaneously recorded. We replicated the pattern of significant, and additive, effects of the 2 manipulations on eye fixation durations. We also replicated the predictability effect on the N400, time-locked to the onset of the reader’s first fixation on the target word. However, there was no indication of a frequency effect in the electroencephalogram record. We suggest that this pattern has implications both for the interpretation of the N400 and for the interpretation of frequency and predictability effects in language comprehension.
Social perception studies have revealed that smiling individuals are perceived more favourably on many communion dimensions in comparison to nonsmiling individuals. Research on gender differences in smiling habits showed that women smile more than men. In our study, we investigated this phenomena further and hypothesised that women perceive smiling individuals as more honest than men. An experiment conducted in seven countries (China, Germany, Mexico, Norway, Poland, Republic of South Africa and USA) revealed that gender may influence the perception of honesty in smiling individuals. We compared ratings of honesty made by male and female participants who viewed photos of smiling and nonsmiling people. While men and women did not differ on ratings of honesty in nonsmiling individuals, women assessed smiling individuals as more honest than men did. We discuss these results from a social norms perspective.
We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use—both individually and collectively—over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.