Measuring the compositionality of noun-noun compounds over time
This work addresses the temporal dynamics of linguistic compositionality for researchers in computational linguistics, but it is incremental as it builds on existing methods with new diachronic data.
The study tackled the problem of measuring how the compositionality of noun-noun compounds changes over time, using the Google Books corpus, and found that temporal information improves prediction of compositionality ratings, though with lower correlation than other corpora, and demonstrated temporal changes for selected compounds.
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.