Abstract
Vector Space Models (VSMs) of Semantics
are useful tools for exploring the semantics of
single words, and the composition of words
to make phrasal meaning. While many methods
can estimate the meaning (i.e. vector) of
a phrase, few do so in an interpretable way.
We introduce a new method (CNNSE) that allows
word and phrase vectors to adapt to the
notion of composition. Our method learns a
VSM that is both tailored to support a chosen
semantic composition operation, and whose
resulting features have an intuitive interpretation.
Interpretability allows for the exploration
of phrasal semantics, which we leverage to analyze
performance on a behavioral task.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
| Publisher | The Association for Computational Linguistics |
| Pages | 32-41 |
| Number of pages | 10 |
| ISBN (Print) | 978-1-941643-49-5 |
| Publication status | Published - Jun 2015 |
| Event | 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) - Denver, United States Duration: 31 May 2015 → 05 Jun 2015 |
Conference
| Conference | 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) |
|---|---|
| Country/Territory | United States |
| City | Denver |
| Period | 31/05/2015 → 05/06/2015 |