# Measuring the compositionality of noun-noun compounds over time

## TL;DR

This study investigates how the compositionality of noun-noun compounds evolves over time using diachronic semantic analysis of Google Books data, revealing temporal variations and the impact of temporal information on predicting compositionality.

## Contribution

It introduces a diachronic approach to measure changes in noun-noun compound compositionality over time using temporal semantic spaces.

## Key findings

- Temporal information improves compositionality prediction.
- Correlation with ratings is lower than in other corpora.
- Observed changes in compositionality for selected compounds over time.

## Abstract

We present work in progress on the temporal progression of compositionality in noun-noun compounds. Previous work has proposed computational methods for determining the compositionality of compounds. These methods try to automatically determine how transparent the meaning of the compound as a whole is with respect to the meaning of its parts. We hypothesize that such a property might change over time. We use the time-stamped Google Books corpus for our diachronic investigations, and first examine whether the vector-based semantic spaces extracted from this corpus are able to predict compositionality ratings, despite their inherent limitations. We find that using temporal information helps predicting the ratings, although correlation with the ratings is lower than reported for other corpora. Finally, we show changes in compositionality over time for a selection of compounds.