Distribution is not enough: going Firther

Andy Lücking, Robin Cooper, Staffan Larsson, Jonathan Ginzburg


Abstract
Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space. In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information. In this paper we argue that the distributional hypothesis is intrinsically misguided as a self-supporting basis for semantics, as Firth was entirely aware. We mention philosophical arguments concerning the lack of normativity within DH data. Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases. Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records. We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.
Anthology ID:
W19-1101
Volume:
Proceedings of the Sixth Workshop on Natural Language and Computer Science
Month:
May
Year:
2019
Address:
Gothenburg, Sweden
Venue:
WS
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/W19-1101
DOI:
10.18653/v1/W19-1101
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W19-1101.pdf