16,678 research outputs found
Thematic Annotation: extracting concepts out of documents
Contrarily to standard approaches to topic annotation, the technique used in
this work does not centrally rely on some sort of -- possibly statistical --
keyword extraction. In fact, the proposed annotation algorithm uses a large
scale semantic database -- the EDR Electronic Dictionary -- that provides a
concept hierarchy based on hyponym and hypernym relations. This concept
hierarchy is used to generate a synthetic representation of the document by
aggregating the words present in topically homogeneous document segments into a
set of concepts best preserving the document's content.
This new extraction technique uses an unexplored approach to topic selection.
Instead of using semantic similarity measures based on a semantic resource, the
later is processed to extract the part of the conceptual hierarchy relevant to
the document content. Then this conceptual hierarchy is searched to extract the
most relevant set of concepts to represent the topics discussed in the
document. Notice that this algorithm is able to extract generic concepts that
are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure
Topic Map Generation Using Text Mining
Starting from text corpus analysis with linguistic and statistical analysis algorithms, an infrastructure for text mining is described which uses collocation analysis as a central tool. This text mining method may be applied to different domains as well as languages. Some examples taken form large reference databases motivate the applicability to knowledge management using declarative standards of information structuring and description. The ISO/IEC Topic Map standard is introduced as a candidate for rich metadata description of information resources and it is shown how text mining can be used for automatic topic map generation
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Clear Visual Separation of Temporal Event Sequences
Extracting and visualizing informative insights from temporal event sequences
becomes increasingly difficult when data volume and variety increase. Besides
dealing with high event type cardinality and many distinct sequences, it can be
difficult to tell whether it is appropriate to combine multiple events into one
or utilize additional information about event attributes. Existing approaches
often make use of frequent sequential patterns extracted from the dataset,
however, these patterns are limited in terms of interpretability and utility.
In addition, it is difficult to assess the role of absolute and relative time
when using pattern mining techniques.
In this paper, we present methods that addresses these challenges by
automatically learning composite events which enables better aggregation of
multiple event sequences. By leveraging event sequence outcomes, we present
appropriate linked visualizations that allow domain experts to identify
critical flows, to assess validity and to understand the role of time.
Furthermore, we explore information gain and visual complexity metrics to
identify the most relevant visual patterns. We compare composite event learning
with two approaches for extracting event patterns using real world company
event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data
Science (VDS), 201
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
Most speech and language technologies are trained with massive amounts of
speech and text information. However, most of the world languages do not have
such resources or stable orthography. Systems constructed under these almost
zero resource conditions are not only promising for speech technology but also
for computational language documentation. The goal of computational language
documentation is to help field linguists to (semi-)automatically analyze and
annotate audio recordings of endangered and unwritten languages. Example tasks
are automatic phoneme discovery or lexicon discovery from the speech signal.
This paper presents a speech corpus collected during a realistic language
documentation process. It is made up of 5k speech utterances in Mboshi (Bantu
C25) aligned to French text translations. Speech transcriptions are also made
available: they correspond to a non-standard graphemic form close to the
language phonology. We present how the data was collected, cleaned and
processed and we illustrate its use through a zero-resource task: spoken term
discovery. The dataset is made available to the community for reproducible
computational language documentation experiments and their evaluation.Comment: accepted to LREC 201
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