19,927 research outputs found
Topic Similarity Networks: Visual Analytics for Large Document Sets
We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We describe efficient and effective approaches to both building and labeling
such networks. Visualizations of topic models based on these networks are shown
to be a powerful means of exploring, characterizing, and summarizing large
collections of unstructured text documents. They help to "tease out"
non-obvious connections among different sets of documents and provide insights
into how topics form larger themes. We demonstrate the efficacy and
practicality of these approaches through two case studies: 1) NSF grants for
basic research spanning a 14 year period and 2) the entire English portion of
Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData
2014
Joint Regression and Ranking for Image Enhancement
Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a given image are found by relating the image to the training images with
known enhancement parameters. While knowing the structure of the parameter
space can facilitate search for the optimal solution, none of the existing
methods has explicitly modeled and learned that structure. This paper presents
an end-to-end, novel joint regression and ranking approach to model the
interaction between desired enhancement parameters and images to be processed,
employing a Gaussian process (GP). GP allows searching for ideal parameters
using only the image features. The model naturally leads to a ranking technique
for comparing images in the induced feature space. Comparative evaluation using
the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
an additional data-set were used to demonstrate the effectiveness of the
proposed approach.Comment: WACV 201
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized representation of a candidate and the misspelling context. To tune
the parameters of this model, we generate self-induced spelling error corpora.
We perform our experiments for two languages. For English, we greatly
outperform off-the-shelf spelling correction tools on a manually annotated
MIMIC-III test set, and counter the frequency bias of a noisy channel model,
showing that neural embeddings can be successfully exploited to improve upon
the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling
correction tool on manually annotated clinical records from the Antwerp
University Hospital, but can offer no empirical evidence that our method
counters the frequency bias of a noisy channel model in this case as well.
However, both our context-sensitive model and our implementation of the noisy
channel model obtain high scores on the test set, establishing a
state-of-the-art for Dutch clinical spelling correction with the noisy channel
model.Comment: Appears in volume 7 of the CLIN Journal,
http://www.clinjournal.org/biblio/volum
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