Me too fragments in English and French

A direct interpretation approach

Datos 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 de correspondencia)
Año2022
Volumen39
Número3
Páginas495-524
Fecha de publicación2022-09-27
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaThe Linguistic Review (JOURNAL)
Identificadores de la revistaISSN: 0167-6318 • E-ISSN: 1613-3676
EditorialWalter de Gruyter GmbH (PUBLISHER • DE)
DOI10.1515/tlr-2022-2095
OpenAlexW4286008855
IdiomaEN
Citas recibidas6
Referencias 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