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The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

Most educational assessments tend to be constructed in a close-ended format, which is easier to score consistently and more affordable. However, recent work has leveraged computation text methods from the information sciences to make open-ended measurement more effective and reliable for older students. The purpose of this study is to determine whether models used by computational text mining applications need to be adapted when used with samples of elementary-aged children.

This study introduces domain-adapted semantic models for child-specific text analysis, to allow better elementary-aged educational assessment. A corpus compiled from a multimodal mix of spoken and written child-directed sources is presented, used to train a children’s language model and evaluated against standard non-age-specific semantic models.

Child-oriented language is found to differ in vocabulary and word sense use from general English, while exhibiting lower gender and race biases. The model is evaluated in an educational application of divergent thinking measurement and shown to improve on generalized English models.

The findings demonstrate the need for age-specific language models in the growing domain of automated divergent thinking and strongly encourage the same for other educational uses of computation text analysis by showing a measurable difference in the language of children.

Understanding children’s language more representatively in automated educational assessment allows for more fair and equitable testing. Furthermore, child-specific language models have fewer gender and race biases.

Research in computational measurement of open-ended responses has thus far used models of language trained on general English sources or domain-specific sources such as textbooks. To the best of the authors’ knowledge, this paper is the first to study age-specific language models for educational assessment. In addition, while there have been several targeted, high-quality corpora of child-created or child-directed speech, the corpus presented here is the first developed with the breadth and scale required for large-scale text modeling.

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of EXtensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user‐defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi‐structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three‐fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a viewdriven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction.

This paper reviews existing business process (BP) modeling languages that are widely used in the industry as well as recent research work on modeling and analysis of BPs in the service-oriented environment and Internetware-based software paradigm. BPs in such environment are different from traditional BPs due to loose coupling of partner services, dynamic and on-the-fly selection of partners and run-time process adaptability. The unique characteristics of these BPs require formal modeling of the requirements and constraints in each phase of their life cycle, including design phase, implementation and deployment phase and execution phase.

The paper first provides a categorization of typical user requirements in each phase of the BP life cycle. Then a detailed comparison of the selected languages with respect to their requirement modeling and analysis capabilities in each of the identified categories is provided. The paper also discusses new requirements engineering research challenges arising from future software needs and emerging trends in software engineering in the context of Web-services-based BPs and Internetware.

There is a need to have a framework that provides support for user requirements modeling and analysis for all the phases of BP life cycle in an integrated manner. Such a framework would be useful not only in resolving the inconsistencies between requirements across phases but also in addressing the issues related to BP evolution due to changes in user requirements over time. Moreover, with the Internet of things (IoT) adoption in BPM, there is a need to have an integrated environment that provides support for capturing the resilience requirements of enterprise BPs as well as the mobility constraints of the underlying IoT devices.

This paper reviews existing BP modeling languages and frameworks and discusses the new requirements engineering research challenges arising from future software needs and the emerging trends in BP management in the service-oriented environment and Internetware-based software paradigm.

In the last two decades, a proliferation of business process management (BPM) modeling languages, standards and software systems has given rise to much confusion and obstacles to adoption. Since new BPM languages and notation terminologies were not well defined, duplicate features are common. This paper seeks to make sense of the myriad BPM standards, organising them in a classification framework, and to identify key industry trends.

An extensive literature review is conducted and relevant BPM notations, languages and standards are referenced against the proposed BPM Standards Classification Framework, which lists each standard's distinct features, strengths and weaknesses.

The paper is unaware of any classification of BPM languages. An attempt is made to classify BPM languages, standards and notations into four main groups: execution, interchange, graphical, and diagnosis standards. At the present time, there is a lack of established diagnosis standards. It is hoped that such a classification facilitates the meaningful adoption of BPM languages, standards and notations.

The paper differentiates BPM standards, thereby resolving common misconceptions; establishes the need for diagnosis standards; identifies the strengths and limitations of current standards; and highlights current knowledge gaps and future trends. Researchers and practitioners may wish to position their work around this review.

Currently, to the best of one's knowledge, such an overview and such an analysis of BPM standards have not so far been undertaken.

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.

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.

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.

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.

Normalization is an important step in all the natural language processing applications that are handling social media text. The text from social media poses a different kind of problems that are not present in regular text. Recently, a considerable amount of work has been done in this direction, but mostly in the English language. People who do not speak English code mixed the text with their native language and posted text on social media using the Roman script. This kind of text further aggravates the problem of normalizing. This paper aims to discuss the concept of normalization with respect to code-mixed social media text, and a model has been proposed to normalize such text.

The system is divided into two phases – candidate generation and most probable sentence selection. Candidate generation task is treated as machine translation task where the Roman text is treated as source language and Gurmukhi text is treated as the target language. Character-based translation system has been proposed to generate candidate tokens. Once candidates are generated, the second phase uses the beam search method for selecting the most probable sentence based on hidden Markov model.

Character error rate (CER) and bilingual evaluation understudy (BLEU) score are reported. The proposed system has been compared with Akhar software and RB\_R2G system, which are also capable of transliterating Roman text to Gurmukhi. The performance of the system outperforms Akhar software. The CER and BLEU scores are 0.268121 and 0.6807939, respectively, for ill-formed text.

It was observed that the system produces dialectical variations of a word or the word with minor errors like diacritic missing. Spell checker can improve the output of the system by correcting these minor errors. Extensive experimentation is needed for optimizing language identifier, which will further help in improving the output. The language model also seeks further exploration. Inclusion of wider context, particularly from social media text, is an important area that deserves further investigation.

The practical implications of this study are: (1) development of parallel dataset containing Roman and Gurmukhi text; (2) development of dataset annotated with language tag; (3) development of the normalizing system, which is first of its kind and proposes translation based solution for normalizing noisy social media text from Roman to Gurmukhi. It can be extended for any pair of scripts. (4) The proposed system can be used for better analysis of social media text. Theoretically, our study helps in better understanding of text normalization in social media context and opens the doors for further research in multilingual social media text normalization.

Existing research work focus on normalizing monolingual text. This study contributes towards the development of a normalization system for multilingual text.

The purpose of this paper is to evaluate and improve two popular business process modelling languages (BPMLs) the Integration definition for function modelling (IDEF0) and event‐driven process chain (eEPC).

The paper aims to select, compare and evaluate against a proposed criteria framework two popular BPMLs. In order to meet end‐user requirements, it suggests concrete improvements for either language. Evaluation findings and improvement attempts are documented over a case study within the context of a large European aerospace industry. The languages are applied through the use of appropriate software such as BPWin and Arena Simulation Software for IDEF0 and Architecture of Integrated Information Systems (ARIS) Toolset for eEPC.

Improved languages seem to overcome several deficiencies and increase their modelling performance. Improvements in IDEF0 include: classification of component information and insertion of logical operators. In this approach the language describes not only the information flow within the process, but also the time sequences via component discrimination. Improvements in eEPC include event chain diagram in ARIS house of business engineering methodology, which represents the interrelations between functions of different diagrams and the linkages among components and resources to include resources planning.

Results showed that language deficiencies were clarified. Thus, the improvements made, constitute better modelling performance and the development of more reliable models.

The paper outlines a systematic evaluation framework with concrete criteria. Furthermore, the improvements made, are a further step towards increased modelling performance, lean and comprehend diagrams.

The paper proposes the development of a graphical architecture description language (ADL) that allows a better understanding of software architectures for nontechnical actors and purposes and, beyond, can serve as a communication tool between domain experts and IT experts, for instance, in a software development process.

The paper follows the methods and guidelines of design science research. By deriving characteristics and general requirements for ADLs from a research literature review and from industry standards, the paper provides a conceptual modeling approach for an ADL. The model design is based on typical requirements and suggestions derived from literature and related work. The application possibilities and advantages are then demonstrated with a usage scenario.

The paper elaborates a user-oriented ADL that makes software architecture comprehensible for stakeholders and end users. It provides a high level of abstraction and, thus, is not restricted to a particular domain. The paper also provides a corresponding modeling editor as well as an underlying catalogue with symbols and rules for the ADL.

As this is a conceptual study, the ADL has not been practically evaluated yet. Thus, the usefulness of this academic approach for the industry remains to be validated.

The elaborated ADL can serve as a language to visualize software architectures, particularly in the business domain, in a comprehensible manner. Still, it retains the structured character of ADLs to facilitate communication on an IT-near level. In including nontechnical actors, the approach broadens the overall application capabilities of ADLs.

One in nine people between the ages of 18 and 64 in the US, and every second foreign-born person in this age bracket, speak Spanish at home. And whereas around 80 percent of adult immigrants in the US from non-English-speaking countries other than Mexico are proficient in English, only about 50 percent of adult immigrants from Mexico are proficient. The use of a language other than English at home, and proficiency in English, are both analyzed in this paper using economic models and data on adult males from the 2000 US Census. The results demonstrate the importance of immigrants’ educational attainment, their age at migration, and years spent in the US to their language skills. The immigrants’ mother tongue is also shown to affect their English proficiency; immigrants with a mother tongue more distant from English being less likely to be proficient. Finally, immigrants living in ethnic–linguistic enclaves have lesser proficiency in English than immigrants who live in predominately English-speaking areas of the US. The results for females are generally very similar to those for males. The findings from an ordered probit approach to estimation are similar to the findings from a binary probit model, and the conclusions drawn from the analyses mirror those in studies based on the 1980 and 1990 US Censuses. Thus, the model of language skills presented appears to be remarkably robust across time and estimation techniques, and between the genders.