Publications

Abstract

Our study contributes to the less explored domain of lexical typology, focusing on semantic prosody and connotation. Semantic derogation, or pejoration of nouns referring to women, whereby such words acquire connotations and further denotations of social pejoration, immorality and/or loose sexuality, has been a very prominent question in studies on gender and language (change). It has been argued that pejoration emerges due to the general derogatory attitudes toward female referents. However, the evidence for systematic differences in connotations of female- vs. male-related words is fragmentary and often fairly impressionistic; moreover, many researchers argue that expressed sentiments toward women (as well as men) often are ambivalent. One should also expect gender differences in connotations to have decreased in the recent years, thanks to the advances of feminism and social progress. We test these ideas in a study of positive and negative connotations of feminine and masculine term pairs such as woman - man, girl - boy, wife - husband, etc. Sentences containing these words were sampled from diachronic corpora of English, Chinese and Russian, and sentiment scores for every word were obtained using two systems for Aspect-Based Sentiment Analysis: PyABSA, and OpenAI’s large language model GPT-3.5. The Generalized Linear Mixed Models of our data provide no indications of significantly more negative sentiment toward female referents in comparison with their male counterparts. However, some of the models suggest that female referents are more infrequently associated with neutral sentiment than male ones. Neither do our data support the hypothesis of the diachronic convergence between the genders. In sum, results suggest that pejoration is unlikely to be explained simply by negative attitudes to female referents in general.

Abstract

Languages differ in the degree of semantic flexibility of their syntactic roles. For example, Eng-
lish and Indonesian are considered more flexible with regard to the semantics of subjects,
whereas German and Japanese are less flexible. In Hawkins’ classification, more flexible lan-
guages are said to have a loose fit, and less flexible ones are those that have a tight fit. This
classification has been based on manual inspection of example sentences. The present paper
proposes a new, quantitative approach to deriving the measures of looseness and tightness from
corpora. We use corpora of online news from the Leipzig Corpora Collection in thirty typolog-
ically and genealogically diverse languages and parse them syntactically with the help of the
Universal Dependencies annotation software. Next, we compute Mutual Information scores for
each language using the matrices of lexical lemmas and four syntactic dependencies (intransi-
tive subjects, transitive subject, objects and obliques). The new approach allows us not only to
reproduce the results of previous investigations, but also to extend the typology to new lan-
guages. We also demonstrate that verb-final languages tend to have a tighter relationship be-
tween lexemes and syntactic roles, which helps language users to recognize thematic roles early
during comprehension.

Abstract

The notion of efficient trade-offs is frequently used in functional linguis-tics in order to explain language use and structure. In this paper I argue that this notion is more confusing than enlightening. Not every negative correlation between parameters represents a real trade-off. Moreover, trade-offs are usually reported between pairs of variables, without taking into account the role of other factors. These and other theoretical issues are illustrated in a case study of linguistic cues used in expressing “who did what to whom”: case marking, rigid word order and medial verb posi-tion. The data are taken from the Universal Dependencies corpora in 30 languages and annotated corpora of online news from the Leipzig Corpora collection. We find that not all cues are correlated negatively, which ques-tions the assumption of language as a zero-sum game. Moreover, the cor-relations between pairs of variables change when we incorporate the third variable. Finally, the relationships between the variables are not always bi-directional. The study also presents a causal model, which can serve as a more appropriate alternative to trade-offs.