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Extracting general or intermediate level terms is a relevant problem that has not received much attention in literature. Current approaches for term extraction rely on contrastive corpora to identify domain-specific terms, which makes them better suited for specialised terms, that are rarely used outside of the domain. In this work, we propose an(More)
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)
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)
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)
In recent years, following the rapid development in the Semantic Web and Knowledge Management research, ontologies have become more in demand in Natural Language Processing. An increasing number of systems use ontologies either internally, for modelling the domain of the application, or as data structures that hold the output resulting from the work of the(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)
One of the most difficult applications of Natural Language Processing (NLP) is text mining (TM) of biomedical abstracts and journal papers. The sheer volume of biomedical research output makes TM a necessity, while the importance of this research requires extremely high retrieval precision. The distributed, confidential, and profitable nature of the(More)