71,948 research outputs found
Clustering documents with active learning using Wikipedia
Wikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instance-level constraints for supervised clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document datasets. Empirical results show that our basic document representation strategy yields comparable performance to previous attempts; and adding constraints improves clustering performance further by up to 20%
LODE: Linking Digital Humanities Content to the Web of Data
Numerous digital humanities projects maintain their data collections in the
form of text, images, and metadata. While data may be stored in many formats,
from plain text to XML to relational databases, the use of the resource
description framework (RDF) as a standardized representation has gained
considerable traction during the last five years. Almost every digital
humanities meeting has at least one session concerned with the topic of digital
humanities, RDF, and linked data. While most existing work in linked data has
focused on improving algorithms for entity matching, the aim of the
LinkedHumanities project is to build digital humanities tools that work "out of
the box," enabling their use by humanities scholars, computer scientists,
librarians, and information scientists alike. With this paper, we report on the
Linked Open Data Enhancer (LODE) framework developed as part of the
LinkedHumanities project. With LODE we support non-technical users to enrich a
local RDF repository with high-quality data from the Linked Open Data cloud.
LODE links and enhances the local RDF repository without compromising the
quality of the data. In particular, LODE supports the user in the enhancement
and linking process by providing intuitive user-interfaces and by suggesting
high-quality linking candidates using tailored matching algorithms. We hope
that the LODE framework will be useful to digital humanities scholars
complementing other digital humanities tools
TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data
Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCAD
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Mitigating Gender Bias in Machine Learning Data Sets
Artificial Intelligence has the capacity to amplify and perpetuate societal
biases and presents profound ethical implications for society. Gender bias has
been identified in the context of employment advertising and recruitment tools,
due to their reliance on underlying language processing and recommendation
algorithms. Attempts to address such issues have involved testing learned
associations, integrating concepts of fairness to machine learning and
performing more rigorous analysis of training data. Mitigating bias when
algorithms are trained on textual data is particularly challenging given the
complex way gender ideology is embedded in language. This paper proposes a
framework for the identification of gender bias in training data for machine
learning.The work draws upon gender theory and sociolinguistics to
systematically indicate levels of bias in textual training data and associated
neural word embedding models, thus highlighting pathways for both removing bias
from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as
part of the ECIR Conference) - http://bias.disim.univaq.i
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