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Distributional semantic models (DSMs) have been effective at representing semantics at the word level, and research has recently moved on to building distributional representations for larger segments of text. In this paper, we introduce novel ways of applying context selection and normalisa-tion to vary model sparsity and the range of values of the DSM(More)
The functional approach to compositional distributional semantics considers transitive verbs to be linear maps that transform the distributional vectors representing nouns into a vector representing a sentence. We conduct an initial investigation that uses a matrix consisting of the parameters of a logistic regression classifier trained on a plausibility(More)
Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited to multiple labels and typically a pre-processing step (normally assigning the majority class) is(More)
This paper presents an interaction-based information filtering system designed for the needs of children accessing multiple streams of information. This is an emerging problem due to the increased information access and engagement by children for their education and entertainment, and the explosion of stream-based information sources on most topics. It has(More)
Compositional distributional semantics is a subfield of Computational Linguistics which investigates methods for representing the meanings of phrases and sentences. In this paper, we explore implementations of a framework based on Combinatory Categorial Grammar (CCG), in which words with certain grammatical types have meanings represented by multi-linear(More)
When undergoing medical treatment in combination with extended stays in hospitals, children have been frequently found to develop an interest in their condition and the course of treatment. A natural means of searching for related information would be to use a web search engine. The medical domain, however, imposes several key challenges on young and(More)
This paper investigates whether the wider context in which a sentence is located can contribute to a distributional representation of sentence meaning. We compare a vector space for sentences in which the features are words occurring within the sentence, with two new vector spaces that only make use of surrounding context. Experiments on simple(More)
Several compositional distributional semantic methods use tensors to model multi-way interactions between vectors. Unfortunately, the size of the tensors can make their use impractical in large-scale implementations. In this paper, we investigate whether we can match the performance of full tensors with low-rank approximations that use a fraction of the(More)