Speechmaking and the Selectorate: Persuasion in Nonpreferential Electoral Systems

2019 ◽  
Vol 53 (5) ◽  
pp. 667-699 ◽  
Author(s):  
Jorge M. Fernandes ◽  
Miguel Won ◽  
Bruno Martins
Keyword(s):  
Mixed Methods Research ◽  
Electoral Systems ◽  
Local Level ◽  
Named Entity Recognition ◽  
Strong Support ◽  
Original Data ◽  
Entity Recognition ◽  
Data Set ◽  
Methodological Innovation ◽  
Elite Interviews

This article examines the extent to which legislators use legislative debates to engage in localism activities to cater to the interests of their selectorate in nonpreferential electoral systems. We define localism activities as the delivery of tangible and intangible benefits to a geographically confined constituency that is instrumental to legislators’ re-selection. Our primary argument is that legislators whose selectorate operates at the local level make more speeches with parochial references. Results show strong support for this assertion. Furthermore, we find that high district magnitude leads to higher levels of localism. We use a mixed-methods research design, combining an original data set of 60,000 debates in Portugal with qualitative evidence from elite interviews. We make a methodological innovation in the field of representation and legislative studies by using a Named Entity Recognition tool to analyze the debates.

scholarly journals Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1178
Author(s):  
Zhenhua Wang ◽  
Beike Zhang ◽  
Dong Gao
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Chemical System ◽  
Chemical Safety ◽  
High Quality ◽  
Data Set ◽  
Final Model ◽  
Named Entity ◽  
Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

scholarly journals Formation and breakdown of the alliance between state and civic forces in post-2013 Egypt: a state-in-society approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazen Hassan
Keyword(s):  
Arab World ◽  
Public Attitudes ◽  
Original Data ◽  
Threat Perception ◽  
Data Set ◽  
Content Type ◽  
The Status ◽  
State Actors ◽  
Elite Interviews ◽  
The One

Purpose This paper aims to examine why the alliance formed between non-Islamist forces and state actors to oust Mohamed Morsi from power in 2013 broke down quickly. Design/methodology/approach This paper makes use of original data set derived from three waves of surveys fielded in 2011, 2014 and 2015 that ask questions about public threat perception. Around 10 elite interviews were also conducted to further test the study’s hypothesis. Findings On the one hand, non-Islamists, civic forces challenged the status and interests of state actors in a way that made state actors view them with heightened distrust. On the other, many civic forces, in face of high threat perception, prioritized law during and order after Morsi’s removal, driven – at least partly – by shifts in public attitudes. Originality/value Many views look at transitions in the Arab world from the angle of how Islamists interact with traditional power holders. Such an approach, however, could be reductionist in many ways because it disregards civic forces. This is a point this paper seeks to address.

scholarly journals Cost-aware active learning for named entity recognition in clinical text

2019 ◽  
Vol 26 (11) ◽  
pp. 1314-1322 ◽  
Author(s):  
Qiang Wei ◽  
Yukun Chen ◽  
Mandana Salimi ◽  
Joshua C Denny ◽  
Qiaozhu Mei ◽  
...  
Keyword(s):  
Active Learning ◽  
Random Sampling ◽  
User Study ◽  
Cost Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Mental Condition ◽  
Noticeable Effect ◽  
Data Set ◽  
Passive Learning

Abstract Objective Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This study introduces a cost-aware AL method, which simultaneously models both the annotation cost and the informativeness of the samples and evaluates both via simulation and user studies. Materials and Methods We designed a novel, cost-aware AL algorithm (Cost-CAUSE) for annotating clinical named entities; we first utilized lexical and syntactic features to estimate annotation cost, then we incorporated this cost measure into an existing AL algorithm. Using the 2010 i2b2/VA data set, we then conducted a simulation study comparing Cost-CAUSE with noncost-aware AL methods, and a user study comparing Cost-CAUSE with passive learning. Results Our cost model fit empirical annotation data well, and Cost-CAUSE increased the simulation area under the learning curve (ALC) scores by up to 5.6% and 4.9%, compared with random sampling and alternate AL methods. Moreover, in a user annotation task, Cost-CAUSE outperformed passive learning on the ALC score and reduced annotation time by 20.5%–30.2%. Discussion Although AL has proven effective in simulations, our user study shows that a real-world environment is far more complex. Other factors have a noticeable effect on the AL method, such as the annotation accuracy of users, the tiredness of users, and even the physical and mental condition of users. Conclusion Cost-CAUSE saves significant annotation cost compared to random sampling.

scholarly journals Making the absent present: Political parties and emigrant issues in country of origin parliaments

2017 ◽  
Vol 25 (2) ◽  
pp. 153-166 ◽  
Author(s):  
Eva Østergaard-Nielsen ◽  
Irina Ciornei
Keyword(s):  
Political Parties ◽  
Conceptual Framework ◽  
Electoral Systems ◽  
Original Data ◽  
Issue Salience ◽  
Substantive Representation ◽  
Data Set ◽  
Electoral Incentives ◽  
The Right ◽  
Legislative Processes

A growing number of countries have granted their emigrant citizens the right to vote in homeland elections from afar. Yet, there is little understanding of the extent to which emigration issues are visible in the subsequent legislative processes of policymaking and representation. Based on an original data set of parliamentary activities in Spain, Italy, France and Romania, this article analyses why political parties pay attention to emigrants. To that end, we propose a conceptual framework which draws on both theories of issue salience and substantive representation. Bridging these two frameworks allows us bring in both parties (salience) and constituencies (representation) in the analysis of the linkage between electorates and parliaments at a transnational level. We test a series of hypotheses and find that parties are more likely to focus on emigration issues the stronger their electoral incentives and in the context of electoral systems allowing the emigrants to elect special emigrant representatives.

The Politics of Private Foreign Aid: Humanitarian Principles, Economic Development Objectives, and Organizational Interests in NGO Private Aid Allocation

2012 ◽  
Vol 66 (4) ◽  
pp. 571-607 ◽  
Author(s):  
Tim Büthe ◽  
Solomon Major ◽  
André de Mello e Souza
Keyword(s):  
Foreign Aid ◽  
Latin American ◽  
Strong Support ◽  
Original Data ◽  
Development Aid ◽  
Aid Effectiveness ◽  
Regional Effect ◽  
Data Set ◽  
Aid Allocation ◽  
Development Ngos

AbstractA large and increasing share of international humanitarian and development aid is raised from nongovernmental sources, allocated by transnational NGOs. We know little about this private foreign aid, not even how it is distributed across recipient countries, much less what explains the allocation. This article presents an original data set, based on detailed financial records from most of the major U.S.-based humanitarian and development NGOs, which allows us for the first time to map and analyze the allocation of U.S. private aid. We find no support for the common claim that aid NGOs systematically prioritize their organizational self-interest when they allocate private aid, and we find only limited support for the hypothesis that expected aid effectiveness drives aid allocation. By contrast, we find strong support for the argument that the deeply rooted humanitarian discourse within and among aid NGOs drives their aid allocation, consistent with a view of aid NGOs as principled actors and constructivist theories of international relations. Recipients' humanitarian need is substantively and statistically the most significant determinant of U.S. private aid allocation (beyond a regional effect in favor of Latin American countries). Materialist concerns do not crowd out ethical norms among these NGOs.

scholarly journals Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision (Preprint)

2021 ◽  
Author(s):  
Qi Jia ◽  
Dezheng Zhang ◽  
Haifeng Xu ◽  
Yonghong Xie
Keyword(s):  
Chinese Medicine ◽  
Gold Standard ◽  
False Negative ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Data Set ◽  
Standard Data ◽  
Named Entity ◽  
Medical Entity ◽  
Clinical Records

BACKGROUND Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. OBJECTIVE Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervised learning methods. Therefore, we utilized distantly supervised named entity recognition (NER) to respond to the challenge. METHODS We propose a span-level distantly supervised NER approach to extract TCM medical entity. It utilizes the pretrained language model and a simple multilayer neural network as classifier to detect and classify entity. We also designed a negative sampling strategy for the span-level model. The strategy randomly selects negative samples in every epoch and filters the possible false-negative samples periodically. It reduces the bad influence from the false-negative samples. RESULTS We compare our methods with other baseline methods to illustrate the effectiveness of our method on a gold-standard data set. The F1 score of our method is 77.34 and it remarkably outperforms the other baselines. CONCLUSIONS We developed a distantly supervised NER approach to extract medical entity from TCM clinical records. We estimated our approach on a TCM clinical record data set. Our experimental results indicate that the proposed approach achieves a better performance than other baselines.

scholarly journals RUREBUS-2020 SHARED TASK: RUSSIAN RELATION EXTRACTION FOR BUSINESS

2020 ◽  
Author(s):  
V. A. Ivanin ◽  
◽  
E. L. Artemova ◽  
T. V. Batura ◽  
V. V. Ivanov ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Relation Extraction ◽  
Business Case ◽  
Entity Recognition ◽  
Quality Data ◽  
Shared Task ◽  
Annotation Scheme ◽  
Data Set ◽  
Named Entity ◽  
Short Time

In this paper, we present a shared task on core information extraction problems, named entity recognition and relation extraction. In contrast to popular shared tasks on related problems, we try to move away from strictly academic rigor and rather model a business case. As a source for textual data we choose the corpus of Russian strategic documents, which we annotated according to our own annotation scheme. To speed up the annotation process, we exploit various active learning techniques. In total we ended up with more than two hundred annotated documents. Thus we managed to create a high-quality data set in short time. The shared task consisted of three tracks, devoted to 1) named entity recognition, 2) relation extraction and 3) joint named entity recognition and relation extraction. We provided with the annotated texts as well as a set of unannotated texts, which could of been used in any way to improve solutions. In the paper we overview and compare solutions, submitted by the shared task participants. We release both raw and annotated corpora along with annotation guidelines, evaluation scripts and results at https://github.com/dialogue-evaluation/RuREBus.

Named Entity Recognition Model of Chinese Clinical Electronic Medical Record Based on XLNet-BiLSTM

2021 ◽  
Author(s):  
Shen Zhou Feng ◽  
Su Qian Min ◽  
Guo Jing Lei
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Vector Model ◽  
Global Maximum ◽  
Data Set ◽  
Recognition Model ◽  
Named Entity ◽  
Feature Sequence

Abstract The recognition of named entities in Chinese clinical electronic medical records is one of the basic tasks to realize smart medical care. Aiming at the insufficient text semantic representation of the traditional word vector model and the inability of the recurrent neural network (RNN) model to solve the problems of long-term dependence, a Chinese clinical electronic medical record named entity recognition model XLNet-BiLSTM-MHA-CRF based on XLNet is proposed. Use the XLNet pre-training language model as the embedding layer to vectorize the medical record text to solve the problem of ambiguity; use the bidirectional long and short-term memory network (BiLSTM) gate control unit to obtain the forward and backward semantic feature information of the sentence; Then input the feature sequence to the multi-head attention layer (multi-head attention, MHA), use MHA to obtain information represented by different subspaces of the feature sequence, enhance the relevance of context semantics and eliminate noise; finally, input the conditional random field CRF to identify the global maximum 优 sequence. The experimental results show that the XLNet-BiLSTM-Attention-CRF model has achieved good results on the CCKS-2017 named entity recognition data set.

Effects of Ballot Type and District Magnitude on Local Public Goods Bill-Initiation Behavior: Evidence from Honduras

2020 ◽  
pp. 106591292090588
Author(s):  
Juan Muñoz-Portillo
Keyword(s):  
Public Goods ◽  
Electoral Systems ◽  
Electoral System ◽  
Original Data ◽  
Local Public Goods ◽  
High Poverty ◽  
Data Set ◽  
Small Magnitude ◽  
District Magnitude ◽  
Seeking Behavior

An influential literature predicts that incentives to provide local public goods are conditioned by how electoral systems expose a legislator to the need to seek a personal vote. Carey and Shugart theorize that district magnitude and ballot type interact affecting the legislators’ personal vote-seeking behavior. Another literature challenges the idea that electoral systems affect the behavior of legislators, particularly in highly clientelist settings, usually associated with high poverty. I empirically evaluate these arguments on an original data set of local goods bills presented by legislators of the National Congress of Honduras between 1990 and 2009. Honduras changed its electoral system from closed-list to open-list in 2004 while keeping its district magnitude constant. The results suggest that the Ballot Type × District Magnitude interaction does not affect the behavior of legislators in small magnitude constituencies, where poverty is more significant. However, support for the hypotheses is found in the largest, more developed constituency where M is equal to twenty-three seats.

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