Jeff Mitchell
University of Bristol
Publications19
Citations1,362
Highly Influential Citations159
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Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional… Continue Reading
This paper proposes a framework for representing the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and… Continue Reading
In this paper we propose a novel statistical language model to capture long-range semantic dependencies. Specifically, we apply the concept of semantic composition to the problem of constructing… Continue Reading
The analysis of reading times can provide insights into the processes that underlie language comprehension, with longer reading times indicating greater cognitive load. There is evidence that the… Continue Reading
In this paper we describe our 2 place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development… Continue Reading
Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here,… Continue Reading
We present a distributional approach to the problem of inducing parameters for unseen words in probabilistic parsers. Our KNN-based algorithm uses distributional similarity over an unlabelled corpus… Continue Reading
A recent distributional approach to wordanalogy problems (Mikolov et al., 2013b) exploits interesting regularities in the structure of the space of representations. Investigating further, we find… Continue Reading
We argue that extrapolation to examples outside the training space will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We… Continue Reading
This paper investigates the extraction of semantic representations from bigrams. The major obstacle to this objective is that while these word to word dependencies do contain a semantic component,… Continue Reading