710,479 research outputs found
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
We present a non-parametric prognostic framework for individualized event
prediction based on joint modeling of both longitudinal and time-to-event data.
Our approach exploits a multivariate Gaussian convolution process (MGCP) to
model the evolution of longitudinal signals and a Cox model to map
time-to-event data with longitudinal data modeled through the MGCP. Taking
advantage of the unique structure imposed by convolved processes, we provide a
variational inference framework to simultaneously estimate parameters in the
joint MGCP-Cox model. This significantly reduces computational complexity and
safeguards against model overfitting. Experiments on synthetic and real world
data show that the proposed framework outperforms state-of-the art approaches
built on two-stage inference and strong parametric assumptions
Deep Recurrent Survival Analysis
Survival analysis is a hotspot in statistical research for modeling
time-to-event information with data censorship handling, which has been widely
used in many applications such as clinical research, information system and
other fields with survivorship bias. Many works have been proposed for survival
analysis ranging from traditional statistic methods to machine learning models.
However, the existing methodologies either utilize counting-based statistics on
the segmented data, or have a pre-assumption on the event probability
distribution w.r.t. time. Moreover, few works consider sequential patterns
within the feature space. In this paper, we propose a Deep Recurrent Survival
Analysis model which combines deep learning for conditional probability
prediction at fine-grained level of the data, and survival analysis for
tackling the censorship. By capturing the time dependency through modeling the
conditional probability of the event for each sample, our method predicts the
likelihood of the true event occurrence and estimates the survival rate over
time, i.e., the probability of the non-occurrence of the event, for the
censored data. Meanwhile, without assuming any specific form of the event
probability distribution, our model shows great advantages over the previous
works on fitting various sophisticated data distributions. In the experiments
on the three real-world tasks from different fields, our model significantly
outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code:
https://github.com/rk2900/drs
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
Necessary and Sufficient Conditions on Partial Orders for Modeling Concurrent Computations
Partial orders are used extensively for modeling and analyzing concurrent
computations. In this paper, we define two properties of partially ordered
sets: width-extensibility and interleaving-consistency, and show that a partial
order can be a valid state based model: (1) of some synchronous concurrent
computation iff it is width-extensible, and (2) of some asynchronous concurrent
computation iff it is width-extensible and interleaving-consistent. We also
show a duality between the event based and state based models of concurrent
computations, and give algorithms to convert models between the two domains.
When applied to the problem of checkpointing, our theory leads to a better
understanding of some existing results and algorithms in the field. It also
leads to efficient detection algorithms for predicates whose evaluation
requires knowledge of states from all the processes in the system
State-space based mass event-history model I: many decision-making agents with one target
A dynamic decision-making system that includes a mass of indistinguishable
agents could manifest impressive heterogeneity. This kind of nonhomogeneity is
postulated to result from macroscopic behavioral tactics employed by almost all
involved agents. A State-Space Based (SSB) mass event-history model is
developed here to explore the potential existence of such macroscopic
behaviors. By imposing an unobserved internal state-space variable into the
system, each individual's event-history is made into a composition of a common
state duration and an individual specific time to action. With the common state
modeling of the macroscopic behavior, parametric statistical inferences are
derived under the current-status data structure and conditional independence
assumptions. Identifiability and computation related problems are also
addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB
mass event-history model is shown to be very distinct from a random effect
model via the Principle Component Analysis (PCA) in a numerical experiment.
Real data showing the mass invasion by two species of parasitic nematode into
two species of host larvae are also analyzed. The analysis results not only are
found coherent in the context of the biology of the nematode as a parasite, but
also include new quantitative interpretations.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS189 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The heavy quark search at the LHC
We explore further the discovery potential for heavy quarks at the LHC, with
emphasis on the and of a sequential fourth family associated with
electroweak symmetry breaking. We consider QCD multijets, ,
and single backgrounds using event generation based on
improved matrix elements and low sensitivity to the modeling of initial state
radiation. We exploit a jet mass technique for the identification of
hadronically decaying 's and 's, to be used in the reconstruction of the
or mass. This along with other aspects of event selection can reduce
backgrounds to very manageable levels. It even allows a search for both
and in the absence of -tagging, of interest for the early running of
the LHC. A heavy quark mass of order 600 GeV is motivated by the connection to
electroweak symmetry breaking, but our analysis is relevant for any new heavy
quarks with weak decay modes.Comment: 12 pages, 7 figure
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