2,615 research outputs found
Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending
This paper presents the 3-space model of conceptual
blending that estimates the figurative similarity between Input
spaces 1 and 2 using both their analogical similarity and the interconnecting
Generic Space. We describe how our Dr Inventor model
is being evaluated as a model of lexically based figurative similarity.
We describe distinct but related evaluation tasks focused on 1)
identifying novel and quality analogies between computer graphics
publications 2) evaluation of machine generated translations of text
documents 3) evaluation of documents in a plagiarism corpus. Our
results show that Dr Inventor is capable of generating novel
comparisons between publications but also appears to be a useful
tool for evaluating machine translation systems and for detecting and
assessing the level of plagiarism between documents. We also
outline another more recent evaluation, using a corpus of patent
applications
Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing
Uncovering novel drug candidates for treating complex diseases remain one of
the most challenging tasks in early discovery research. To tackle this
challenge, biopharma research established a standardized high content imaging
protocol that tags different cellular compartments per image channel. In order
to judge the experimental outcome, the scientist requires knowledge about the
channel importance with respect to a certain phenotype for decoding the
underlying biology. In contrast to traditional image analysis approaches, such
experiments are nowadays preferably analyzed by deep learning based approaches
which, however, lack crucial information about the channel importance. To
overcome this limitation, we present a novel approach which utilizes
multi-spectral information of high content images to interpret a certain aspect
of cellular biology. To this end, we base our method on image blending concepts
with alpha compositing for an arbitrary number of channels. More specifically,
we introduce DCMIX, a lightweight, scaleable and end-to-end trainable mixing
layer which enables interpretable predictions in high content imaging while
retaining the benefits of deep learning based methods. We employ an extensive
set of experiments on both MNIST and RXRX1 datasets, demonstrating that DCMIX
learns the biologically relevant channel importance without scarifying
prediction performance.Comment: Accepted @ DAGM German Conference on Pattern Recognition (GCPR) 202
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
We propose a simple approach for the abstractive summarization of long legal
opinions that considers the argument structure of the document. Legal opinions
often contain complex and nuanced argumentation, making it challenging to
generate a concise summary that accurately captures the main points of the
legal opinion. Our approach involves using argument role information to
generate multiple candidate summaries, then reranking these candidates based on
alignment with the document's argument structure. We demonstrate the
effectiveness of our approach on a dataset of long legal opinions and show that
it outperforms several strong baselines
Artificial Intelligence methodologies to early predict student outcome and enrich learning material
L'abstract è presente nell'allegato / the abstract is in the attachmen
Us and them: identifying cyber hate on Twitter across multiple protected characteristics
Hateful and antagonistic content published and propagated via the World Wide Web
has the potential to cause harm and suffering on an individual basis, and lead to
social tension and disorder beyond cyber space. Despite new legislation aimed at
prosecuting those who misuse new forms of communication to post threatening,
harassing, or grossly offensive language - or cyber hate - and the fact large social
media companies have committed to protecting their users from harm, it goes largely
unpunished due to difficulties in policing online public spaces. To support the
automatic detection of cyber hate online, specifically on Twitter, we build multiple
individual models to classify cyber hate for a range of protected characteristics
including race, disability and sexual orientation. We use text parsing to extract typed
dependencies, which represent syntactic and grammatical relationships between
words, and are shown to capture ‘othering’ language - consistently improving
machine classification for different types of cyber hate beyond the use of a Bag of
Words and known hateful terms. Furthermore, we build a data-driven blended model
of cyber hate to improve classification where more than one protected characteristic
may be attacked (
e.g.
race and sexual orientation), contributing to the nascent study
of intersectionality in hate crime
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