Me too fragments in English and French

A direct interpretation approach

Dados Bibliográficos

ID20344128
AutoresAlexis Abeille (0000-0002-9187-2298, Laboratoire de Linguistique Formelle , Université Paris Cité , Paris , France), J B Kim (0000-0003-3286-0446, Kyung Hee University, autor correspondente)
Ano2022
Volume39
Fascículo3
Páginas495-524
Data de publicação2022-09-27
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoThe Linguistic Review (JOURNAL)
Identificadores do periódicoISSN: 0167-6318 • E-ISSN: 1613-3676
EditoraWalter de Gruyter GmbH (PUBLISHER • DE)
DOI10.1515/tlr-2022-2095
OpenAlexW4286008855
IdiomaEN
Citações recebidas6
Referências citadas28

Additive fragments, comprising a nominal remnant and an additive adverb (e.g., too , either ), are a particular type of stripping. On the basis of new corpus data in English and French, we show that such fragments do not always have a verbal clause as their antecedent, and that when they do, different kinds of mismatch are possible between a verbal equivalent and the actual fragment. This challenges most approaches based on syntactic reconstruction. We also show that their interpretation is more flexible than previously thought, since they can be used for interrogative, exclamatory, or ordering purposes. We distinguish between their contrastive (non-coreferent) use ( A: John left. B: Me too .) and emphatic (coreferent) use (A: John left. B: HIM too!). We propose a direct interpretation analysis that resorts to no syntactic reconstruction of a verbal clause. The proposed analysis, developed within the framework of construction-based HPSG, allows us to capture not only their properties sharing with other fragments (short answers and negative stripping) but also their unique constructional properties

Adverb · Algorithm · Generative grammar · Head-driven phrase structure grammar · Interrogative · Linguistics · Natural language processing · Computer Science · Language, Metaphor, and Cognition · Natural Language Processing Techniques · Philosophy · Psychology · Syntax, Semantics, Linguistic Variation · Artificial Intelligence