Name
Title | ||
|---|---|---|
Verbal Multiword Expression Identification - Do We Need a Sledgehammer to Crack a Nut? |
Abstract | ||
|---|---|---|
Automatic identification of multiword expressions (MWEs), like to cut corners \u0027to do an incomplete job \u0027, is a prerequisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general. |
Year | Venue | DocType |
|---|---|---|
2020 | COLING | Conference |
Volume | Citations | PageRank |
2020.coling-main | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caroline Pasquer | 1 | 0 | 2.03 |
| Agata Savary | 2 | 92 | 19.55 |
| carlos ramisch | 3 | 161 | 22.91 |
| Jean-Yves Antoine | 4 | 142 | 25.37 |