scholarly journals Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model

2019 ◽  
Author(s):  
Junchi Zhang ◽  
Yanxia Qin ◽  
Yue Zhang ◽  
Mengchi Liu ◽  
Donghong Ji
Keyword(s):  
State Transition ◽  
Transition Process ◽  
Neural Model ◽  
Joint Model ◽  
Event Extraction ◽  
Traditional Methods ◽  
Single Task ◽  
Output Structure ◽  
Complex Joint ◽  
Argument Roles

The task of event extraction contains subtasks including detections for entity mentions, event triggers and argument roles. Traditional methods solve them as a pipeline, which does not make use of task correlation for their mutual benefits. There have been recent efforts towards building a joint model for all tasks. However, due to technical challenges, there has not been work predicting the joint output structure as a single task. We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process. Results on standard benchmarks show the benefits of the joint model, which gives the best result in the literature.

Globally normalized neural model for joint entity and event extraction

2021 ◽  
Vol 58 (5) ◽  
pp. 102636
Author(s):  
Junchi Zhang ◽  
Wenzhi Huang ◽  
Donghong Ji ◽  
Yafeng Ren
Keyword(s):  
Neural Model ◽  
Event Extraction

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

scholarly journals Model-based kernel sum rule: kernel Bayesian inference with probabilistic models

2020 ◽  
Vol 109 (5) ◽  
pp. 939-972
Author(s):  
Yu Nishiyama ◽  
Motonobu Kanagawa ◽  
Arthur Gretton ◽  
Kenji Fukumizu
Keyword(s):  
Bayesian Inference ◽  
Probabilistic Model ◽  
Probabilistic Models ◽  
State Transition ◽  
Hybrid Approach ◽  
Transition Process ◽  
The State ◽  
Nonparametric Inference ◽  
Sum Rule ◽  
Model Based

AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

scholarly journals Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Chang Liu ◽  
Jie Xue ◽  
Xu Cheng ◽  
Weiwei Zhan ◽  
Xin Xiong ◽  
...  
Keyword(s):  
Conversion Rate ◽  
State Transition ◽  
Transition Process ◽  
Brain State ◽  
Healthy Children ◽  
Time Varying ◽  
The Brain ◽  
Function Connectivity ◽  
Time Point ◽  
Research Studies

BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.

Integrating the Behavioral and Neural Dynamics of Response Selection in a Dual-task Paradigm: A Dynamic Neural Field Model of Dux et al. ()

2014 ◽  
Vol 26 (2) ◽  
pp. 334-351 ◽  
Author(s):  
Aaron T. Buss ◽  
Tim Wifall ◽  
Eliot Hazeltine ◽  
John P. Spencer
Keyword(s):  
Cognitive Control ◽  
Dual Task ◽  
Hebbian Learning ◽  
Response Selection ◽  
Neural Model ◽  
Neural Basis ◽  
Single Task ◽  
Neural Field Model ◽  
Sensory Motor ◽  
Task Trial

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. ( 2009 ) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

scholarly journals A Neural Model for Joint Event Detection and Summarization

2017 ◽  
Author(s):  
Zhongqing Wang ◽  
Yue Zhang
Keyword(s):  
Event Detection ◽  
Neural Model ◽  
Representation Learning ◽  
Joint Model ◽  
Large Group ◽  
Joint Event

Twitter new event detection aims to identify first stories in a tweet stream. Typical approaches consider two sub tasks. First, it is necessary to filter out mundane or irrelevant tweets. Second, tweets are grouped automatically into event clusters. Traditionally, these two sub tasks are processed separately, and integrated under a pipeline setting, despite that there is inter-dependence between the two tasks. In addition, one further related task is summarization, which is to extract a succinct summary for representing a large group of tweets. Summarization is related to detection, under the new event setting in that salient information is universal between event representing tweets and informative event summaries. In this paper, we build a joint model to filter, cluster, and summarize the tweets for new events. In particular, deep representation learning is used to vectorize tweets, which serves as basis that connects tasks. A neural stacking model is used for integrating a pipeline of different sub tasks, and for better sharing between the predecessor and successors. Experiments show that our proposed neural joint model is more effective compared to its pipeline baseline.

Agile Coaches and Champions

2016 ◽  
pp. 24-36
Author(s):  
Taghi Javdani Gandomani ◽  
Mina Ziaei Nafchi
Keyword(s):  
Software Development ◽  
Large Scale ◽  
Critical Role ◽  
Transition Process ◽  
Agile Methods ◽  
Traditional Methods ◽  
Software Companies ◽  
The People ◽  
Agile Approach

Prevalence of Agile methods in software companies is increasing dramatically. Software companies and teams need to employ these methods to overcome the inherent challenges of traditional methods in software development. However, transitioning to Agile approach is a topic of debate. This is mainly because software companies are facing with many challenges, obstacles, and hindrances when leaving traditional methods and moving to Agile methods, as shown in previous research studies. Conducting a large-scale research study showed that Agile transformation need to be supported by several facilitators and identified its most important facilitators. The main aim of this chapter is to present two hidden facilitators of Agile transition, Agile coaches and Agile champions, which rarely have been taken into consideration. Both of these facilitators directly impress the people involved in the transition. People-intensive nature of Agile methods and critical role of the people in the transition process reflect the importance of these facilitators when a software company doing its transition.

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