Papers by Caroline Pasquer
Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut? (2020.coling-main)
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| Challenge: | Multiword expressions (MWEs) are word combinations idiosyncratic with respect to syntax or semantics. |
| Approach: | They propose to use a language-independent system to identify previously seen VMWEs by combining filters to obtain the best averaged F-score over 11 languages and the best score for both seen and unseen VMwes. |
| Outcome: | The proposed system obtains the best averaged F-score over 11 languages and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMwes in texts. |
If you’ve seen some, you’ve seen them all: Identifying variants of multiword expressions (C18-1)
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| Challenge: | Multiword expressions (VMWEs) show idiosyncratic variability, which is challenging for NLP applications. |
| Approach: | They propose to use a model to identify variants of previously seen VMWEs by comparing VMWAs with morpho-syntactic variations. |
| Outcome: | The proposed approach outperforms a baseline by 4 percent points of F-measure on a French corpus. |
Towards a Variability Measure for Multiword Expressions (N18-2)
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| Challenge: | Multiword expressions (MWEs) are groups of words whose meaning does not derive from the meaning of their components and from their syntactic structure in a regular way. |
| Approach: | They propose to use a language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues to assess its relevance with respect to a linguistic benchmark. |
| Outcome: | The proposed measure is useful for VMWE classification and variant identification on a French corpus. |