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The purpose of this paper is to introduce an interpreting schema and semantic description framework for a collection of images of Xilankapu, a traditional Chinese form of embroidered fabric and brocade artwork.

First, the authors interpret the artwork of Xilankapu through Gillian Rose’s “four site” theory by presenting how the brocades were made, how the patterns of Xilankapu are classified and the geometrical abstraction of visual images. To further describe the images of this type of brocade, this paper presents semantic descriptions that include objective–non-objective relations and a multi-layered semantic framework. Furthermore, the authors developed corresponding methods for scanning, storage and indexing images for retrieval.

As exploratory research on describing, preserving and indexing images of Xilankapu in the context of the preservation of cultural heritage, the authors collected 1,000+ images of traditional Xilankapu, classifying and storing some of the images in a database. They developed an index schema that combines concept- and content-based approaches according to the proposed semantic description framework. They found that the framework can describe, store and preserve semantic and non-semantic information of the same image. They relate the findings of this paper to future research directions for the digital preservation of traditional cultural heritages.

The framework has been designed especially for brocade, and it needs to be extended to other types of cultural image.

The semantic description framework can describe connotative semantic information on Xilankapu. It can also assist the later information retrieval work in organizing implicit information about culturally related visual materials.

The newspaper is a very widely used medium for transmitting news and information in today's society. Some of the published information is very important and therefore required to be kept for future reference. The cutting and subsequent retrieval of newspaper articles is a tedious process. The retrieval process is also very laborious. Electronic newspaper cutting is required to replace the manual process. A library newspaper cutting system was developed at Nanyang Technological University, Singapore. The system enables librarians to cut, index and store news articles interactively. Readers can retrieve news articles via World Wide Web (WWW) browsers. Different abstract generation techniques, including location method, indicative‐phrases, keyword frequency and title‐keyword method, are incorporated into the retrieval interface to filter out irrelevant news articles. In this paper, different abstract generation techniques and their effectiveness as a means to generate indicative abstracts for news articles are discussed.

After considering the search process and functions of index entries, a classification of entry types is offered, based on index term context, predominant term order, and between‐term function words. Then a multiple entry generation scheme is described, comprising rules for term manipulation, input and output. After discussing access points and cross reference measures, a preliminary linguistic analysis is given, showing links with psycholinguistics. The study forms the basis of a current laboratory investigation (EPSILON) into a number of entry types.

Many students in the United States struggle to achieve proficiency in writing. Writing is an important skill to develop, as it is a way for students to communicate what they know and integrate knowledge and critical thinking skills. A lack of writing proficiency can have a significant impact on academic performance in secondary school and on postsecondary outcomes. Improving writing instruction requires theoretically sound, scientifically validated teaching practices, including assessments and instructional methods. It also requires that teachers are well prepared to implement such practices, including using assessment data to tailor instructional methods to meet the needs of students who experience significant writing difficulties. The purpose of this chapter is to provide an overview of advances in research and practice related to validated teaching practices designed to improve the writing outcomes of students with intensive needs, and to describe an innovative way to prepare and support teachers to implement such practices.

Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited, as they are constructed from the occurrence of accidents. This paper aims to propose a new approach to facilitate narrative generation from existing narrative sources to support training and learning.

A computational narrative semi-fiction generation (CNSG) approach is proposed, and a case study was conducted in a statutory body in the construction industry in Hong Kong. Apart from measuring the learning outcomes gained by participants through the new narratives, domain experts were invited to evaluate the performance of the CNSG approach.

The performance of the CNSG approach is found to be effective in facilitating new narrative generation from existing narrative sources and to generate synthetic semi-fiction narratives to support and educate individuals to learn from past lessons. The new narratives generated by the CNSG approach help students learn and remember important things and learning points from the narratives. Domain experts agree that the validated narratives are useful for training and learning purposes.

This study presents a new narrative generation process for a high-risk industry, e.g. the construction industry. The CNSG approach incorporates the technologies of natural language processing and artificial intelligence to computationally identify narrative gaps in existing narrative sources and proposes narrative fragments to generate new semi-fiction narratives. Encouraging results were gained through the case study.

The purpose of this paper is to present an algorithm to generate story problems via controlled parameters in the domain of mathematics. The generation process is performed in the problem generation module in the context of an intelligent tutoring system suggested in this paper. Controlling the question parameters allows for adapting the generated questions according to the specific student needs. Story problems are selected since they are one of the most important types of problems in mathematics, as they help train students to apply their knowledge to real-world problems. Such problems target improving different student’s skills including literacy skills through reading the problem, recognizing the embedded mathematical information, and applying the required arithmetic operators.

Natural language generation (NLG) techniques are used to control the difficulty level of the generated story problem header in addition to effecting variations from the natural language point of view. The proposed NLG technique is based on different separated knowledge categories to provide flexibility in the generation process and allow porting the module to other contexts, domains, and to other natural languages without a complete redesign.

The approach has been empirically evaluated, and the results show that the generated problems are sound, clear, and naturally readable. This is in addition to the usability of the tutoring system itself.

The generation technique is confined to the problem described using rhetorical schemas. Nevertheless, it can generate any problem provided that the rhetorical schema is available.

Most story problems generation systems limit the variation of the story problems to formulating the sentences that describe the story problem and the associated mathematical operations. In contrast, this paper presents a story problems generation technique that allows variations in the structure of the narrative story as well as the context, sentences, wordings, and mathematical operations. This variability allows assessing different student skills along different dimensions with gradually increasing difficulty levels.

According to the Indian Sign Language Research and Training Centre (ISLRTC), India has approximately 300 certified human interpreters to help people with hearing loss. This paper aims to address the issue of Indian Sign Language (ISL) sentence recognition and translation into semantically equivalent English text in a signer-independent mode.

This study presents an approach that translates ISL sentences into English text using the MobileNetV2 model and Neural Machine Translation (NMT). The authors have created an ISL corpus from the Brown corpus using ISL grammar rules to perform machine translation. The authors’ approach converts ISL videos of the newly created dataset into ISL gloss sequences using the MobileNetV2 model and the recognized ISL gloss sequence is then fed to a machine translation module that generates an English sentence for each ISL sentence.

As per the experimental results, pretrained MobileNetV2 model was proven the best-suited model for the recognition of ISL sentences and NMT provided better results than Statistical Machine Translation (SMT) to convert ISL text into English text. The automatic and human evaluation of the proposed approach yielded accuracies of 83.3 and 86.1%, respectively.

It can be seen that the neural machine translation systems produced translations with repetitions of other translated words, strange translations when the total number of words per sentence is increased and one or more unexpected terms that had no relation to the source text on occasion. The most common type of error is the mistranslation of places, numbers and dates. Although this has little effect on the overall structure of the translated sentence, it indicates that the embedding learned for these few words could be improved.

Sign language recognition and translation is a crucial step toward improving communication between the deaf and the rest of society. Because of the shortage of human interpreters, an alternative approach is desired to help people achieve smooth communication with the Deaf. To motivate research in this field, the authors generated an ISL corpus of 13,720 sentences and a video dataset of 47,880 ISL videos. As there is no public dataset available for ISl videos incorporating signs released by ISLRTC, the authors created a new video dataset and ISL corpus.

This paper aims to propose a system that generates dialogue scenarios automatically in real time from Web news articles. Then, the authors used the Manzai metaphor, a form of Japanese traditional humorous comedy, in the system. The generated Manzai scenario consists of snappy patter and a humorous misunderstanding of dialogue based on the gap of our structure of funny points. The authors create communication robots to amuse people with the generated humorous robot dialogue scenarios.

The authors propose the following: how to generate funny dialogue-based scenario from Web news and Web intelligence, automatically? How to create direction of robots based on the pre-experiment? The authors conducted experiments from three viewpoints, namely, effectiveness of Manzai scenarios as content, effectiveness of Manzai-Robots as a medium and familiarity of Manzai-Robots.

In this paper, the authors find two points, namely, the new communication style called “human–robots implicit communication-and bridging the knowledge gap using Web intelligence, to communicate smoothly between humans and robots.

Numerous studies have examined communication robots that mutually communicate with people. However, for several reasons, communicating smoothly with people is difficult for robots. One reason is the problem of communication style. Another is knowledge gaps separating humans and robots. The authors propose a new communication style to solve the problems and designate the communication style based on dialogue between robots as “human-robot implicit communication”. The authors then create communication robots to communicate with people naturally, smoothly and with familiarity according to their dialogue.

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.

The recent report for the Commission of the European Communities on current multilingual activities in the field of scientific and technical information and the 1977 conference on the same theme both included substantial sections on operational and experimental machine translation systems, and in its Plan of action the Commission announced its intention to introduce an operational machine translation system into its departments and to support research projects on machine translation. This revival of interest in machine translation may well have surprised many who have tended in recent years to dismiss it as one of the ‘great failures’ of scientific research. What has changed? What grounds are there now for optimism about machine translation? Or is it still a ‘utopian dream’ ? The aim of this review is to give a general picture of present activities which may help readers to reach their own conclusions. After a sketch of the historical background and general aims (section I), it describes operational and experimental machine translation systems of recent years (section II), it continues with descriptions of interactive (man‐machine) systems and machine‐assisted translation (section III), (and it concludes with a general survey of present problems and future possibilities section IV).