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Academic research into entrepreneurship policy is particularly interesting due to the increasing relevance of the topic and since knowledge about the evolution of themes in this field is still rather limited. The purpose of this paper is to analyse the key concepts, topics, trends and shifts that have shaped the entrepreneurship policy research agenda during the period 1990–2016.

This paper uses text mining techniques, cluster analysis and complementary bibliographic data to examine the evolution of a corpus of 1,048 academic papers focused on entrepreneurship-related policies and published during the period 1990–2016 in ten relevant journals. In particular, the paper follows a standard text mining workflow: first, as text is unstructured, content requires a set of pre-processing tasks and then a stemming process. Then, the paper examines the most repeated concepts within the corpus, considering the whole period 1990–2016 and also in five-year terms. Finally, the paper conducts a k-means clustering to divide the collection of documents into coherent groups with similar content. The analyses in the paper also include geographical particularities considering three regional sub-corpora, distinguishing those articles authored in the European Union (EU), the USA and South and Eastern Asia, respectively.

Results of the analysis show that inclusion, employment and regulation-related papers have largely dominated the research in the field, evolving from an initial classical approach to the relationship between entrepreneurship and employment to a wider, multidisciplinary perspective, including the relevance of management, geographies and narrower topics such as agglomeration economics or internationalisation instead of the previous generic sectorial approaches. The text mining analysis also reveals how entrepreneurship policy research has gained increasing attention and has become both more open, with a growing cooperation among researchers from different affiliations, and more sophisticated, with concepts and themes that moved the research agenda forward, closer to the priorities of policy implementation.

The paper identifies main trends and research gaps in the field of entrepreneurship policy providing actionable knowledge by presenting the spectrum of both over-explored and understudied research themes in the field. In practical terms the results of the text mining analysis can be interpreted as a compass to navigate the entrepreneurship policy research agenda.

The paper presents the heterogeneity of topics under research in the field, reinforcing the concept of entrepreneurship as a multidisciplinary and dynamic domain. Therefore, the definition and adoption of a certain policy agenda in entrepreneurship should consider multiple aspects (needs, objectives, stakeholders, expected outputs, etc.) to be comprehensive and aligned with its complexity. In addition, the paper shows how text mining techniques could be used to map the research activity in a particular field, contributing to the challenge of linking research and policy.

The exploratory nature of text mining allows us to obtain new knowledge and reveals hidden patterns from large quantities of documents/text data, representing an opportunity to complement other qualitative reviews. In this sense, the main value of this paper is not to advise on the future configuration of entrepreneurship policy as a research topic, but to unwrap the past by unveiling how key themes of the entrepreneurship policy research agenda have emerged, evolved and/or declined over time as a foundation on which to build further developments.

Topic segmentation is one of the active research fields in natural language processing. Also, many topic segmenters have been proposed. However, the current challenge of researchers is the improvement of these segmenters by using external resources. Therefore, the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.

New topic segmenters (TSS-Onto and TSB-Onto) are proposed based on the two well-known segmenters C99 and TextTiling. The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource. Subsequently, an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.

Based on this study, the authors showed that adding semantic knowledge, which is extracted from a domain ontology, improves the quality of topic segmentation. Moreover, TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.

The main limitation of this study is the used test corpus for the evaluation which is not a benchmark. However, we used a collection of scientific papers from well-known digital libraries (ArXiv and ACM).

The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.

The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge. This knowledge is extracted from an ontological external resource.

The purpose of this paper is to evaluate the effectiveness of the information retrieval component of a daily newspaper publisher’s integrated library system (ILS) in comparison with the open source alternatives and observe the impact of the scale of metadata, generated daily by library administrators, on retrieved result sets.

In Experiment 1, the authors compared the result sets of the information retrieval system (IRS) component of the publisher’s current ILS and the result sets of proposed ones with human-assessed relevance judgment set. In Experiment 2, the authors compared the performance of proposed IRS components with the publisher’s current production IRS, using result sets of current IRS classified as relevant. Both experiments were conducted using standard information retrieval (IR) evaluation methods: precision, recall, precision at k, F-measure, mean average precision and 11-point interpolated average precision.

Results showed that: first, in Experiment 1, the publisher’s current production ILS ranked last of all participating IRSs when compared to a relevance document set classified by the senior library administrator; and second, in Experiment 2, the tested IR components’ request handlers that used only automatically generated metadata performed slightly better than request handlers that used all of the metadata fields. Therefore, regarding the effectiveness of IR, the daily human effort of generating the publisher’s current set of metadata attributes is unjustified.

The experiments’ collections contained Slovene language with large number of variations of the forms of nouns, verbs and adjectives. The results could be different if the experiments’ collections contained languages with different grammatical properties.

The authors have confirmed, using standard IR methods, that the IR component used in the publisher’s current ILS, could be adequately replaced with an open source component. Based on the research, the publisher could incorporate the suggested open source IR components in practice. In the research, the authors have described the methods that can be used by libraries for evaluating the effectiveness of the IR of their ILSs.

The paper provides a framework for the evaluation of an ILS’s IR effectiveness for libraries. Based on the evaluation results, the libraries could replace the IR components if their current information system setup allows it.

Vocabulary learning is a difficult task for children without hearing ability. Absence of enough learning centers and effective learning tools aggravate the problem. Modern technology can be utilized fruitfully to find solutions to the learning difficulties experienced by the deaf. The purpose of this paper is to present SiLearn – a novel technology based tool for teaching/learning sign vocabulary.

The proposed mobile application can act as a visual dictionary for deaf people. SiLearn is equipped with features that can automatically detect both text and physical objects and convert them to their corresponding signs. For testing the effectiveness of the proposed mobile application quantitative analyses were done. Quantitative analysis is based on testing a class of 28 students belonging to St Clare Oral School for the Deaf, Kerala, India. This group consisted of 17 boys and 11 girls. Analysis was also done through questionnaire. Questionnaires were given to teachers, parents of deaf students learning sign language and other sign language learners.

Results indicate that as SiLearn is very effective in sign vocabulary development. It can enhance vocabulary learning rate considerably.

This is the first time that artificial intelligence (AI) based techniques are used for early stage sign language learning. SiLearn can equally be used by children, parents and teachers for learning sign language.

The paper aims to propose a method to build environmental constraint region online in complex-shaped peg-in-hole assembly tasks.

Compared with conventional way which using computer-aided design (CAD) models of assembly parts to construct the environmental constraint region offline, the paper provides an online approach that consists of three aspects: modeling assembly parts through visual recognition, decomposing complex shapes into multiple primitive convex shapes and a numerical algorithm to simulate the peg-in-hole insertion contact. Besides, a contrast experiment is performed to validate the feasibility and effectiveness of the method.

The experiment result indicates that online construction takes less time than the offline way under the same task conditions. Furthermore, due to the CAD models of the parts are not required to be known, the method proposed in the paper has a broader application in most assembly scenarios.

With the improvement of customization and complexity of manufactured parts, the assembly of complex-shaped parts has drawn greater attention of many researchers. The assembly methods based on attractive region in environment (ARIE) have shown great performance to achieve high-precision manipulation with low-precision systems. The construction of environmental constraint region serves as an essential part of ARIE-based theory, directly affect the formulation and application of assembly strategies.

The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules.

The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together.

The proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works.

The stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results.

In this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts.

The use of structured literature review methods like bibliometric analysis is growing in the management fields, but there is limited knowledge on how they can be facilitated by technology. Hence, we conducted a broad overview of software tools, their roles, and limitations in structured (bibliometric) literature reviewing activities. Subsequently, we show that several software tools are freely available to aid in searching the literature, identifying/ extracting relevant publications, screening/assessing quality of the extracted data, and performing analyses to generate insights from the literature. However, their applications may be confronted with several challenges such as limited analytical and functional capabilities, inadequate technological skills of researchers, and the fact that the researcher's insights are still needed to generate compelling conclusions from the results produced by software tools. Consequently, we contribute toward advancing the methodologies for performing structured reviews by providing a comprehensive and updated overview of the knowledge base of key technological software tools and the conduct of structured or bibliometric literature reviews.

Mobile Internet applications on ubiquitous mobile networks allows real‐time, anywhere, anytime connectivity to services. Owing to its scalability and potential cost savings, mobile communication is being increasingly applied in the business and consumer communities to create innovative data and voice application, which run over the Internet infrastructure. This paper reports on a case study at an organisation that created an innovative approach to developing mobile applications developed by third party independent developers. A conceptual wireless reference model is presented that was used to define the various system constituents required to create effective mobile applications.

The problem of morphological analysis may be considered as minor for languages like English compared to languages with a rich inflectional system like Greek. The plural inflection of the English noun which, apart few exceptions, is very simple (add ‐s) while in modern Greek there are 41 different inflectional suffixes. Storage of all the inflected forms of lexical items appearing in a text would cause an explosion in terms of storage space and retrieval time. There are different forms of the written Greek language: such forms include classical Greek, formal language and modern Greek. In this work we examine modern Greek texts.

The purpose of this paper is to develop a text extraction tool for scanned documents that would extract text and build the keywords corpus and key phrases corpus for the document without manual intervention.

For text extraction from scanned documents, a Web-based optical character recognition (OCR) tool was developed. OCR is a well-established technology, so to develop the OCR, Microsoft Office document imaging tools were used. To account for the commonly encountered problem of skew being introduced, a method to detect and correct the skew introduced in the scanned documents was developed and integrated with the tool. The OCR tool was customized to build keywords and key phrases corpus for every document.

The developed tool was evaluated using a 100 document corpus to test the various properties of OCR. The tool had above 99 per cent word read accuracy for text only image documents. The customization of the OCR was tested with samples of Microfiches, sample of Journal pages from back volumes and samples from newspaper clips and the results are discussed in the summary. The tool was found to be useful for text extraction and processing.

The scanned documents are converted to keywords and key phrases corpus. The tool could be used to build metadata for scanned documents without manual intervention.

The tool is used to convert unstructured data (in the form of image documents) to structured data (the document is converted into keywords, and key phrases database). In addition, the image document is converted to editable and searchable document.