Multi-word Measures: Modeling Semantic Change in Compound Nouns (2025)

Abstract: Compound words (e.g. shower thought) provide a multifaceted challenge for diachronic models of semantic change. Datasets describing noun compound semantics tend to describe only the predominant sense of a compound, which is limiting, especially in diachronic settings where senses may shift over time. We create a novel dataset of relatedness judgements of noun compounds in English and German, the first to capture diachronic meaning changes for multi-word expressions without prematurely condensing individual senses into an aggregate value. Furthermore, we introduce a novel, sense-targeting approach for noun compounds that evaluates two contrasting vector representations in their ability to cluster example sentence pairs. Our clustering approach targets both noun compounds and their constituent parts, to model the interdependence of these terms over time. We calculate time-delineated distributions of these clusters and compare them against measures of semantic change aggregated from the human relatedness annotations.

## introduction novel uses of language (coining new senses of words and phrases) are readily available at all times, but not necessarily 'sticky' to the point that they see widespread adoption, making the new sense apparent in a sample of text. in addressing this challenge, models of language change are aided by comparing terms that may have changed against reference points assumed not to have changed. compound nouns in english and german provide an exciting test ground of hypothesized changing and reference terms for computational models of semantic change, due to their sustained productivity in both languages and diverse sets of possible relationships between the meanings of the constituent words of a compound, and the compound as a whole. we compare changes and continuities in nouncompound semantics from a historical perspective, comparing the extent to which a compound like gold mine might differ in its usage over time compared with both its constituent words (gold, mine) and with the use of further compounds that share a constituent (e.g. silver mine, which lacks the broader metaphorical sense that gold mine hassee the first row of unrelated "he never returned to his architectural gold mine of the prewar period." related "one morning they saw a workman standing on the brick wall, who looked about him as if quite at leisure..." given that we do not know in advance with how many senses a compound of interest was used across ≈200 years, we use clustering methods 1 to find groups of contexts that are similar. we employ contextual word embeddings to represent the target compounds, because they allow each target to have multiple representations in the same space. this lets us compare the contexts of compound nouns across time periods, and to predict whether the distribution of each target differs sufficiently to constitute an overall change (via broadening or narrowing the available set of senses). importantly, the development of these contexts is not considered in isolation, but rather in terms of changes in the contexts of the compound's constituent nouns, or in terms of sets of noun compounds that share either the same modifier or head constituent. while most prior evaluations of semantic change models only use aggregate quantitative ratings of target words, we propose a multi-faceted assessment. we evaluate our clustering approaches in terms of (1) accuracy against pairwise goldstandard example sentences that we annotated for semantic relatedness, (2) with respect to multiple measures of semantic change, which we obtained by comparing average relatedness ratings across time periods, and (3) by inducing individual senses by clustering the graph of annotated examples. we provide the following contributions: (1) a novel dataset of relatedness ratings for english and german noun compounds across diachronic contexts. (2) expansion of the lexical semantic change and semantic-relatedness clustering tasks to complex lexical items (i.e., noun compounds). (3) contrasting evaluation methods using in-context semantic relatedness ratings, and measures of semantic change in aggregate. ## prior work the precise definition of 'compound' can be difficult to arrive at (e.g. via constraints of structural integrity), especially cross-linguistically. we take after @xcite to view compounds as 'wordlike' (functioning as a whole like a word), while being composed of more than one 'word' whose contributions to the overall meaning we can analyze separately. ## corpora and annotated data in this section we introduce the diachronic corpora that our clustering models operate over, as well as the annotated gold-standard data that we produced to evaluate the models. ## clustering pipeline we cluster our target compounds with the goal of matching the semantic relatedness judgments. we use the k-means algorithm foot_4 to cluster contextsensitive vector representations of our target terms in order to group instances with related meanings together, and to separate unrelated meanings. the value of k was estimated experimentally for each run, by using silhouette scores @xcite to select a value of k between 4 and 32 foot_5 . the vector representation and the set of related terms that are clustered along with the target compounds both vary across experiments.