3,798 research outputs found
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
Political perspective detection has become an increasingly important task
that can help combat echo chambers and political polarization. Previous
approaches generally focus on leveraging textual content to identify stances,
while they fail to reason with background knowledge or leverage the rich
semantic and syntactic textual labels in news articles. In light of these
limitations, we propose KCD, a political perspective detection approach to
enable multi-hop knowledge reasoning and incorporate textual cues as
paragraph-level labels. Specifically, we firstly generate random walks on
external knowledge graphs and infuse them with news text representations. We
then construct a heterogeneous information network to jointly model news
content as well as semantic, syntactic and entity cues in news articles.
Finally, we adopt relational graph neural networks for graph-level
representation learning and conduct political perspective detection. Extensive
experiments demonstrate that our approach outperforms state-of-the-art methods
on two benchmark datasets. We further examine the effect of knowledge walks and
textual cues and how they contribute to our approach's data efficiency.Comment: accepted at NAACL 2022 main conferenc
A Labeled Graph Kernel for Relationship Extraction
In this paper, we propose an approach for Relationship Extraction (RE) based
on labeled graph kernels. The kernel we propose is a particularization of a
random walk kernel that exploits two properties previously studied in the RE
literature: (i) the words between the candidate entities or connecting them in
a syntactic representation are particularly likely to carry information
regarding the relationship; and (ii) combining information from distinct
sources in a kernel may help the RE system make better decisions. We performed
experiments on a dataset of protein-protein interactions and the results show
that our approach obtains effectiveness values that are comparable with the
state-of-the art kernel methods. Moreover, our approach is able to outperform
the state-of-the-art kernels when combined with other kernel methods
Ghetto of Venice: Access to the Target Node and the Random Target Access Time
Random walks defined on undirected graphs assign the absolute scores to all
nodes based on the quality of path they provide for random walkers. In city
space syntax, the notion of segregation acquires a statistical interpretation
with respect to random walks. We analyze the spatial network of Venetian canals
and detect its most segregated part which can be identified with canals
adjacent to the Ghetto of Venice.Comment: 14 pages, 3 figure
On the Effect of Semantically Enriched Context Models on Software Modularization
Many of the existing approaches for program comprehension rely on the
linguistic information found in source code, such as identifier names and
comments. Semantic clustering is one such technique for modularization of the
system that relies on the informal semantics of the program, encoded in the
vocabulary used in the source code. Treating the source code as a collection of
tokens loses the semantic information embedded within the identifiers. We try
to overcome this problem by introducing context models for source code
identifiers to obtain a semantic kernel, which can be used for both deriving
the topics that run through the system as well as their clustering. In the
first model, we abstract an identifier to its type representation and build on
this notion of context to construct contextual vector representation of the
source code. The second notion of context is defined based on the flow of data
between identifiers to represent a module as a dependency graph where the nodes
correspond to identifiers and the edges represent the data dependencies between
pairs of identifiers. We have applied our approach to 10 medium-sized open
source Java projects, and show that by introducing contexts for identifiers,
the quality of the modularization of the software systems is improved. Both of
the context models give results that are superior to the plain vector
representation of documents. In some cases, the authoritativeness of
decompositions is improved by 67%. Furthermore, a more detailed evaluation of
our approach on JEdit, an open source editor, demonstrates that inferred topics
through performing topic analysis on the contextual representations are more
meaningful compared to the plain representation of the documents. The proposed
approach in introducing a context model for source code identifiers paves the
way for building tools that support developers in program comprehension tasks
such as application and domain concept location, software modularization and
topic analysis
Context-aware Path Ranking for Knowledge Base Completion
Knowledge base (KB) completion aims to infer missing facts from existing ones
in a KB. Among various approaches, path ranking (PR) algorithms have received
increasing attention in recent years. PR algorithms enumerate paths between
entity pairs in a KB and use those paths as features to train a model for
missing fact prediction. Due to their good performances and high model
interpretability, several methods have been proposed. However, most existing
methods suffer from scalability (high RAM consumption) and feature explosion
(trains on an exponentially large number of features) problems. This paper
proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems
by introducing a selective path exploration strategy. C-PR learns global
semantics of entities in the KB using word embedding and leverages the
knowledge of entity semantics to enumerate contextually relevant paths using
bidirectional random walk. Experimental results on three large KBs show that
the path features (fewer in number) discovered by C-PR not only improve
predictive performance but also are more interpretable than existing baselines
Scaling and Universality in City Space Syntax: between Zipf and Matthew
We report about universality of rank-integration distributions of open spaces
in city space syntax similar to the famous rank-size distributions of cities
(Zipf's law). We also demonstrate that the degree of choice an open space
represents for other spaces directly linked to it in a city follows a power law
statistic. Universal statistical behavior of space syntax measures uncovers the
universality of the city creation mechanism. We suggest that the observed
universality may help to establish the international definition of a city as a
specific land use pattern.Comment: 24 pages, 5 *.eps figure
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