Most diachronic studies on both lexico-semantic change and political language usage are based on individual or comparable corpora. In this paper, we explore ways of studying the stability (and changeability) of lexical usage in political discourse across two corpora which are substantially different in structure and size. We present a case study focusing on lexical items associated with political parties in two diachronic corpora of Austrian German, namely a diachronic media corpus (AMC) and a corpus of parliamentary records (ParlAT), and measure the cross-temporal stability of lexical usage over a period of 20 years. We conduct three sets of comparative analyses investigating a) the stability of sets of lexical items associated with the three major political parties over time, b) lexical similarity between parties, and c) the similarity between the lexical choices in parliamentary speeches by members of the parties vis-‘a-vis the media’s reporting on the parties. We employ time series modeling using generalized additive models (GAMs) to compare the lexical similarities and differences between parties within and across corpora. The results show that changes observed in these measures can be meaningfully related to political events during that time.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.