Abstract
We present a novel approach for the identification of multiword expressions (MWEs). The methodology extracts a large set of recurring syntactic fragments from a given treebank using a Tree-Kernel method. Differently from previous studies, the expressions underlying these fragments are arbitrarily long and can
include intervening gaps. In the initial study we use these fragments to identify MWEs as a parsing task (in a supervised manner) as proposed by Green et al. (2011). Here we obtain a small improvement over previous results. In
the second part, we compare various association measures in reranking the expressions underlying these fragments in an unsupervised fashion. We show how a newly defined measure (Log Inside Ratio) based on statistical
parsing techniques is able to outperform classical association measures in the French data.
include intervening gaps. In the initial study we use these fragments to identify MWEs as a parsing task (in a supervised manner) as proposed by Green et al. (2011). Here we obtain a small improvement over previous results. In
the second part, we compare various association measures in reranking the expressions underlying these fragments in an unsupervised fashion. We show how a newly defined measure (Log Inside Ratio) based on statistical
parsing techniques is able to outperform classical association measures in the French data.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th Workshop on Multiword Expressions |
| Place of Publication | Denver |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 10-18 |
| Number of pages | 9 |
| DOIs | |
| Publication status | Published - Jun-2015 |
| Externally published | Yes |