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Found 16 papers, 3 papers with code
Re-solve it: simulating the acquisition of core semantic competences from small data
Many tasks are considered to be {`}solved{'} in the computational linguistics literature, but the corresponding algorithms operate in ways which are radically different from human cognition.
Evaluating the Consistency of Word Embeddings from Small Data
Specifically, we inspect the behaviour of models using a pre-trained background space in learning.
From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding
Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data.
Towards Incremental Learning of Word Embeddings Using Context Informativeness
In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way.
Distributional Semantics in the Real World: Building Word Vector Representations from a Truth-Theoretic Model
There are two main aspects to this difference: a) DSMs are built over corpus data which may or may not reflect {`}what is in the world{'}; b) they are built from word co-occurrences, that is, from lexical types rather than entities and sets.
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking
Knowing the state-of-the-art for a particular task is an essential component of any computational linguistics investigation.
Predictability of Distributional Semantics in Derivational Word Formation
Compositional distributional semantic models (CDSMs) have successfully been applied to the task of predicting the meaning of a range of linguistic constructions.
`Calling on the classical phone': a distributional model of adjective-noun errors in learners' English
In this paper we discuss three key points related to error detection (ED) in learners{'} English.
