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1 – 10 of over 2000The purpose of this study was to describe effective clinical supervisory behavior as perceived by school principals and to contrast the findings with other current studies of…
Abstract
The purpose of this study was to describe effective clinical supervisory behavior as perceived by school principals and to contrast the findings with other current studies of clinical supervision. Three aspects of a principal's supervisory behavior were studied, the verbal behavior used by the principal with the teacher in post‐observation lesson analysis sessions, the basis of authority the principal had over the teachers, and the frequency of clinical supervisory behavior. Sixty‐five principals completed a Q‐sort that described eight different supervisors and rated them from most to least effective. Only the principal's supervisory verbal behavior was perceived as related to the perception of effective clinical supervision.
Qihang Wu, Daifeng Li, Lu Huang and Biyun Ye
Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between…
Abstract
Purpose
Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.
Design/methodology/approach
This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.
Findings
Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.
Originality/value
The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.
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Richard J. Hughbank and Leland C. Horn
The concept of leadership is an oft-discussed issue among practitioners and scholars alike without regard to culture, background, or organizational affiliation. Based on our…
Abstract
The concept of leadership is an oft-discussed issue among practitioners and scholars alike without regard to culture, background, or organizational affiliation. Based on our international experiences, leadership is an art that is traditionally taught as a science which is impacted via various psychological concepts. It is both a natural phenomenon and a learned attribute that is planted, nurtured, developed, and tested over time. Certain leadership approaches are formal, only succeeding in formal settings and environments while others are dependent upon conditioning of the leader. Regardless of one’s leadership style and characteristics, it is critical that both leaders and followers define and understand the variances between failure and success within an organization. This chapter addresses international leadership styles and the psychological theories that support differing approaches assisting the reader to more clearly understand and identify the subtle differences in the development of a successful leader and organization from global perspectives.
Pengcheng Li, Qikai Liu, Qikai Cheng and Wei Lu
This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised…
Abstract
Purpose
This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.
Design/methodology/approach
Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.
Findings
In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.
Originality/value
This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.
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Giovanna Aracri, Antonietta Folino and Stefano Silvestri
The purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction…
Abstract
Purpose
The purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system.
Design/methodology/approach
A method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision.
Findings
The study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation.
Originality/value
The paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.
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Johannes Braun, Jochen Hausler and Wolfgang Schäfers
The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.
Abstract
Purpose
The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.
Design/methodology/approach
By means of an artificial neural network, market sentiment is extracted from 66,070 US real estate market news articles from the S&P Global Market Intelligence database. For training of the network, a distant supervision approach utilizing 17,822 labeled investment ideas from the crowd-sourced investment advisory platform Seeking Alpha is applied.
Findings
According to the results of autoregressive distributed lag models including contemporary and lagged sentiment as independent variables, the derived textual sentiment indicator is not only significantly linked to the depth and resilience dimensions of market liquidity (proxied by Amihud’s (2002) price impact measure), but also to the breadth dimension (proxied by transaction volume).
Practical implications
These results suggest an intertemporal effect of sentiment on liquidity for the direct property market. Market participants should account for this effect in terms of their investment decisions, and also when assessing and pricing liquidity risk.
Originality/value
This paper not only extends the literature on text-based sentiment indicators in real estate, but is also the first to apply artificial intelligence for sentiment extraction from news articles in a market liquidity setting.
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Thommie Burström and Mattias Jacobsson
The purpose of this paper is to describe and analyze the liaison role of project controllers in new product development (NPD) projects.
Abstract
Purpose
The purpose of this paper is to describe and analyze the liaison role of project controllers in new product development (NPD) projects.
Design/methodology/approach
This paper is based on a case study of an industrial new product development project. In total, 68 in‐depth interviews were conducted and 32 meetings were observed. Using an inductive approach, this paper scrutinizes the roles of three specific individuals – their formal role as project controllers and their informal role as liaisons.
Findings
The study found that project controllers play a crucial part in the everyday work of projects – both formally and informally. Project controllers undertake important liaison activities that are not a part of their formal roles in which they extend their responsibilities to include informal activities such as peacekeeping, probing, nailing, process implementation and streamlining.
Practical implications
This paper argues that managers must identify and acknowledge the importance of informal liaisons and liaison activities among project members because such activities are of crucial importance for the facilitation of communication and for work‐flow coordination. By viewing the project controller as someone who is “dressing the project in numbers”, the role can be understood as a support function aimed at close interaction and cross‐functional learning, rather than a function aimed at distant supervision and control.
Originality/value
This paper provides important insights into informal aspects of project roles and the everyday work of project controllers.
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Todor Mihaylov, Tsvetomila Mihaylova, Preslav Nakov, Lluís Màrquez, Georgi D. Georgiev and Ivan Kolev Koychev
The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user…
Abstract
Purpose
The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community forums.
Design/methodology/approach
The authors are aiming to build a classifier to distinguish trolls vs regular users. Unfortunately, it is not easy to get reliable training data. The authors solve this issue pragmatically: the authors assume that a user who is called a troll by several people is likely to be such, which are called accused trolls. Based on this assumption and on leaked reports about actual paid opinion manipulation trolls, the authors build a classifier to distinguish trolls vs regular users.
Findings
The authors compare the profiles of paid trolls vs accused trolls vs non-trolls, and show that a classifier trained to distinguish accused trolls from non-trolls does quite well also at telling apart paid trolls from non-trolls.
Research limitations/implications
The troll detection works even for users with about 10 comments, but it achieves the best performance for users with a sizable number of comments in the forum, e.g. 100 or more. Yet, there is not such a limitation for troll comment detection.
Practical implications
The approach would help forum moderators in their work, by pointing them to the most suspicious users and comments. It would be also useful to investigative journalists who want to find paid opinion manipulation trolls.
Social implications
The authors can offer a better experience to online users by filtering out opinion manipulation trolls and their comments.
Originality/value
The authors propose a novel approach for finding paid opinion manipulation trolls and their posts.
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Guo Chen, Jiabin Peng, Tianxiang Xu and Lu Xiao
Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by…
Abstract
Purpose
Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association.
Design/methodology/approach
This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations.
Findings
This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends.
Originality/value
This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.
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Chuanming Yu, Haodong Xue, Manyi Wang and Lu An
Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From…
Abstract
Purpose
Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages.
Design/methodology/approach
This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction.
Findings
The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages.
Originality/value
The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.
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