225 research outputs found
Network Capacity Bound for Personalized PageRank in Multimodal Networks
In a former paper the concept of Bipartite PageRank was introduced and a
theorem on the limit of authority flowing between nodes for personalized
PageRank has been generalized. In this paper we want to extend those results to
multimodal networks. In particular we introduce a hypergraph type that may be
used for describing multimodal network where a hyperlink connects nodes from
each of the modalities. We introduce a generalisation of PageRank for such
graphs and define the respective random walk model that can be used for
computations. we finally state and prove theorems on the limit of outflow of
authority for cases where individual modalities have identical and distinct
damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373
Supervised and unsupervised methods for learning representations of linguistic units
Word representations, also called word embeddings, are generic representations, often high-dimensional vectors. They map the discrete space of words into a continuous vector space, which allows us to handle rare or even unseen events, e.g. by considering the nearest neighbors. Many Natural Language Processing tasks can be improved by word representations if we extend the task specific training data by the general knowledge incorporated in the word representations.
The first publication investigates a supervised, graph-based method to create word representations. This method leads to a graph-theoretic similarity measure, CoSimRank, with equivalent formalizations that show CoSimRankâs close relationship to Personalized Page-Rank and SimRank. The new formalization is efficient because it can use the graph-based word representation to compute a single node similarity without having to compute the similarities of the entire graph. We also show how we can take advantage of fast matrix multiplication algorithms.
In the second publication, we use existing unsupervised methods for word representation learning and combine these with semantic resources by learning representations for non-word objects like synsets and entities. We also investigate improved word representations which incorporate the semantic information from the resource. The method is flexible in that it can take any word representations as input and does not need an additional training corpus. A sparse tensor formalization guarantees efficiency and parallelizability.
In the third publication, we introduce a method that learns an orthogonal transformation of the word representation space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We use ultradense representations for a Lexicon Creation task in which words are annotated with three types of lexical information â sentiment, concreteness and frequency.
The final publication introduces a new calculus for the interpretable ultradense subspaces, including polarity, concreteness, frequency and part-of-speech (POS). The calculus supports operations like ââ1 Ă hate = loveâ and âgive me a neutral word for greasyâ (i.e., oleaginous) and extends existing analogy computations like âking â man + woman = queenâ.WortreprĂ€sentationen, sogenannte Word Embeddings, sind generische ReprĂ€sentationen, meist hochdimensionale Vektoren. Sie bilden den diskreten Raum der Wörter in einen stetigen Vektorraum ab und erlauben uns, seltene oder ungesehene Ereignisse zu behandeln -- zum Beispiel durch die Betrachtung der nĂ€chsten Nachbarn. Viele Probleme der Computerlinguistik können durch WortreprĂ€sentationen gelöst werden, indem wir spezifische Trainingsdaten um die allgemeinen Informationen erweitern, welche in den WortreprĂ€sentationen enthalten sind.
In der ersten Publikation untersuchen wir ĂŒberwachte, graphenbasierte Methodenn um WortreprĂ€sentationen zu erzeugen. Diese Methoden fĂŒhren zu einem graphenbasierten ĂhnlichkeitsmaĂ, CoSimRank, fĂŒr welches zwei Ă€quivalente Formulierungen existieren, die sowohl die enge Beziehung zum personalisierten PageRank als auch zum SimRank zeigen. Die neue Formulierung kann einzelne KnotenĂ€hnlichkeiten effektiv berechnen, da graphenbasierte WortreprĂ€sentationen benutzt werden können.
In der zweiten Publikation verwenden wir existierende WortreprĂ€sentationen und kombinieren diese mit semantischen Ressourcen, indem wir ReprĂ€sentationen fĂŒr Objekte lernen, welche keine Wörter sind, wie zum Beispiel Synsets und EntitĂ€ten. Die FlexibilitĂ€t unserer Methode zeichnet sich dadurch aus, dass wir beliebige WortreprĂ€sentationen als Eingabe verwenden können und keinen zusĂ€tzlichen Trainingskorpus benötigen.
In der dritten Publikation stellen wir eine Methode vor, die eine Orthogonaltransformation des Vektorraums der WortreprĂ€sentationen lernt. Diese Transformation fokussiert relevante Informationen in einen ultra-kompakten Untervektorraum. Wir benutzen die ultra-kompakten ReprĂ€sentationen zur Erstellung von WörterbĂŒchern mit drei verschiedene Angaben -- Stimmung, Konkretheit und HĂ€ufigkeit.
Die letzte Publikation prĂ€sentiert eine neue Rechenmethode fĂŒr die interpretierbaren ultra-kompakten UntervektorrĂ€ume -- Stimmung, Konkretheit, HĂ€ufigkeit und Wortart. Diese Rechenmethode beinhaltet Operationen wie ââ1 Ă Hass = Liebeâ und âneutrales Wort fĂŒr Winkeladvokatâ (d.h., Anwalt) und erweitert existierende Rechenmethoden, wie âOnkel â Mann + Frau = Tanteâ
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
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