11,616 research outputs found
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
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Using latent-semantic analysis and network analysis for monitoring conceptual development
This paper describes and evaluates CONSPECT (from concept inspection), an application that analyses states in a learnerâs conceptual development. It was designed to help online learners and their tutors monitor conceptual development and also to help reduce the workload of tutors monitoring a learnerâs conceptual development. CONSPECT combines two technologies - Latent Semantic Analysis (LSA) and Network Analysis (NA) into a technique called Meaningful Interaction Analysis (MIA). LSA analyses the meaning in the textual digital traces left behind by learners in their learning journey; NA provides the analytic instrument to investigate (visually) the semantic structures identiïŹed by LSA. This paper describes the validation activities undertaken to show how well LSA matches ïŹrst year medical students in 1) grouping similar concepts and 2) annotating text
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process
A literature survey of methods for analysis of subjective language
Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area
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