14,313 research outputs found
Word Embeddings for Entity-annotated Texts
Learned vector representations of words are useful tools for many information
retrieval and natural language processing tasks due to their ability to capture
lexical semantics. However, while many such tasks involve or even rely on named
entities as central components, popular word embedding models have so far
failed to include entities as first-class citizens. While it seems intuitive
that annotating named entities in the training corpus should result in more
intelligent word features for downstream tasks, performance issues arise when
popular embedding approaches are naively applied to entity annotated corpora.
Not only are the resulting entity embeddings less useful than expected, but one
also finds that the performance of the non-entity word embeddings degrades in
comparison to those trained on the raw, unannotated corpus. In this paper, we
investigate approaches to jointly train word and entity embeddings on a large
corpus with automatically annotated and linked entities. We discuss two
distinct approaches to the generation of such embeddings, namely the training
of state-of-the-art embeddings on raw-text and annotated versions of the
corpus, as well as node embeddings of a co-occurrence graph representation of
the annotated corpus. We compare the performance of annotated embeddings and
classical word embeddings on a variety of word similarity, analogy, and
clustering evaluation tasks, and investigate their performance in
entity-specific tasks. Our findings show that it takes more than training
popular word embedding models on an annotated corpus to create entity
embeddings with acceptable performance on common test cases. Based on these
results, we discuss how and when node embeddings of the co-occurrence graph
representation of the text can restore the performance.Comment: This paper is accepted in 41st European Conference on Information
Retrieva
A Network Topology Approach to Bot Classification
Automated social agents, or bots, are increasingly becoming a problem on
social media platforms. There is a growing body of literature and multiple
tools to aid in the detection of such agents on online social networking
platforms. We propose that the social network topology of a user would be
sufficient to determine whether the user is a automated agent or a human. To
test this, we use a publicly available dataset containing users on Twitter
labelled as either automated social agent or human. Using an unsupervised
machine learning approach, we obtain a detection accuracy rate of 70%
Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions
Genetic regulatory networks (GRNs) have been widely studied, yet there is a
lack of understanding with regards to the final size and properties of these
networks, mainly due to no network currently being complete. In this study, we
analyzed the distribution of GRN structural properties across a large set of
distinct prokaryotic organisms and found a set of constrained characteristics
such as network density and number of regulators. Our results allowed us to
estimate the number of interactions that complete networks would have, a
valuable insight that could aid in the daunting task of network curation,
prediction, and validation. Using state-of-the-art statistical approaches, we
also provided new evidence to settle a previously stated controversy that
raised the possibility of complete biological networks being random and
therefore attributing the observed scale-free properties to an artifact
emerging from the sampling process during network discovery. Furthermore, we
identified a set of properties that enabled us to assess the consistency of the
connectivity distribution for various GRNs against different alternative
statistical distributions. Our results favor the hypothesis that highly
connected nodes (hubs) are not a consequence of network incompleteness.
Finally, an interaction coverage computed for the GRNs as a proxy for
completeness revealed that high-throughput based reconstructions of GRNs could
yield biased networks with a low average clustering coefficient, showing that
classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking
We present WISER, a new semantic search engine for expert finding in
academia. Our system is unsupervised and it jointly combines classical language
modeling techniques, based on text evidences, with the Wikipedia Knowledge
Graph, via entity linking.
WISER indexes each academic author through a novel profiling technique which
models her expertise with a small, labeled and weighted graph drawn from
Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the
author's publications, whereas the weighted edges express the semantic
relatedness among these entities computed via textual and graph-based
relatedness functions. Every node is also labeled with a relevance score which
models the pertinence of the corresponding entity to author's expertise, and is
computed by means of a proper random-walk calculation over that graph; and with
a latent vector representation which is learned via entity and other kinds of
structural embeddings derived from Wikipedia.
At query time, experts are retrieved by combining classic document-centric
approaches, which exploit the occurrences of query terms in the author's
documents, with a novel set of profile-centric scoring strategies, which
compute the semantic relatedness between the author's expertise and the query
topic via the above graph-based profiles.
The effectiveness of our system is established over a large-scale
experimental test on a standard dataset for this task. We show that WISER
achieves better performance than all the other competitors, thus proving the
effectiveness of modelling author's profile via our "semantic" graph of
entities. Finally, we comment on the use of WISER for indexing and profiling
the whole research community within the University of Pisa, and its application
to technology transfer in our University
Correlated fragile site expression allows the identification of candidate fragile genes involved in immunity and associated with carcinogenesis
Common fragile sites (cfs) are specific regions in the human genome that are
particularly prone to genomic instability under conditions of replicative
stress. Several investigations support the view that common fragile sites play
a role in carcinogenesis. We discuss a genome-wide approach based on graph
theory and Gene Ontology vocabulary for the functional characterization of
common fragile sites and for the identification of genes that contribute to
tumour cell biology. CFS were assembled in a network based on a simple measure
of correlation among common fragile site patterns of expression. By applying
robust measurements to capture in quantitative terms the non triviality of the
network, we identified several topological features clearly indicating
departure from the Erdos-Renyi random graph model. The most important outcome
was the presence of an unexpected large connected component far below the
percolation threshold. Most of the best characterized common fragile sites
belonged to this connected component. By filtering this connected component
with Gene Ontology, statistically significant shared functional features were
detected. Common fragile sites were found to be enriched for genes associated
to the immune response and to mechanisms involved in tumour progression such as
extracellular space remodeling and angiogenesis. Our results support the
hypothesis that fragile sites serve a function; we propose that fragility is
linked to a coordinated regulation of fragile genes expression.Comment: 18 pages, accepted for publication in BMC Bioinformatic
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