Search results

The purpose of this paper is to look into the latest advances in ontology-based text summarization systems, with emphasis on the methodologies of a socio-cognitive approach, the structural discourse models and the ontology-based text summarization systems.

The paper analyzes the main literature in this field and presents the structure and features of Texminer, a software that facilitates summarization of texts on Port and Coastal Engineering. Texminer entails a combination of several techniques, including: socio-cognitive user models, Natural Language Processing, disambiguation and ontologies. After processing a corpus, the system was evaluated using as a reference various clustering evaluation experiments conducted by Arco (2008) and Hennig et al. (2008). The results were checked with a support vector machine, Rouge metrics, the F-measure and calculation of precision and recall.

The experiment illustrates the superiority of abstracts obtained through the assistance of ontology-based techniques.

The authors were able to corroborate that the summaries obtained using Texminer are more efficient than those derived through other systems whose summarization models do not use ontologies to summarize texts. Thanks to ontologies, main sentences can be selected with a broad rhetorical structure, especially for a specific knowledge domain.

The purpose of this paper is to enhance the quality of new reducing sentence in sentence-generation-based summarizing method by establishing consequence relationship between two action, state or process Vietnamese sentences.

First, types of pairs of Vietnamese sentences based on presupposition about the consequence relationship is classified: the verb indicating action or state at the first sentence is considered as the consequence of the verb indicating action, state or process at the second sentence. Then main predicates in Discourse Representation Structure – a logical form which represents the semantic of a given pair of sentences – is analyzed and inner- and inter-sentential relationships are determined. The next step is to generate the syntactic structure of the new reducing sentence. Finally, a combination with the built set of lexicons is done to complete the new meaning-summarizing Vietnamese sentence.

This method makes the new meaning-summarizing Vietnamese sentence satisfy two requirements: summarize the semantic of the given pair of Vietnamese sentences and have naturalism in common Vietnamese communication. In addition, it is possible to extend the method and apply for the purpose of summarizing the more complex Vietnamese paragraphs as well as paragraphs in other languages.

At the first step, only inter-sentential consequence relationship is considered and this is applied to the limit types of pairs of Vietnamese sentences which have a simple structure.

This study presents improvements in sentence-generation-based summarization method to enhance the quality of new meaning-summarizing Vietnamese sentences. This method proves effective in summarizing the considered pairs of sentences.

Producing meeting documents requires an instantaneous recorder during meetings, which costs extra human resources and takes time to amend the file. However, a high-quality meeting document can enable users to recall the meeting content efficiently. The paper aims to discuss these issues.

An application based on this framework is developed to help the users find topics and obtain summarizations of meeting contents without extra effort. This app uses the Bluemix speech recognizer to obtain speech transcripts. It then combines latent Dirichlet allocation and a TextTiling algorithm with the speech script of meetings to detect boundaries between different topics and evaluate the topics in each segment. TextTeaser, an open API based on a feature-based approach, is then used to summarize the speech transcripts.

The results indicate that the summaries generated by the machine are 85 percent similar to the records written by humankind.

To reduce the human effort in generating meeting reports, this paper presents a framework to record and analyze meeting contents automatically by voice recognition, topic detection, and extractive summarization.

Purpose – To provide educators with an overview of strategies that can be incorporated into clinical settings that foster vocabulary and comprehension development.

Design/methodology/approach – The chapter highlights underlying themes of reading failure, benefits of large vocabularies and comprehension skills, and components for remediation/instruction.

Findings – Content provides detailed information on designing clinics that prepare students to meet the vocabulary and comprehension demands of reading in the 21st century.

Research limitations/implications – The chapter highlights the most reliable and practical reading strategies that are fundamental to every reader's advancement.

Practical implications – This chapter serves as a resource for all clinical instructors, providing a wealth of ideas for incorporation into their clinics and classrooms.

Originality/value of paper – This compilation of vocabulary and comprehension strategies works in tandem to produce highly skilled readers who can in turn learn independently.

The goal of this paper is to contribute to the development of both data localization and description techniques in P2P systems.

The approach consists of introducing a novel indexing technique that relies on linguistic data summarization into the context of P2P systems.

The cost model of the approach, as well as the simulation results have shown that the approach allows the efficient maintenance of data summaries, without incurring high traffic overhead. In addition, the cost of query routing is significantly reduced in the context of summaries.

The paper has considered a summary service defined on the APPA's architecture. Future works have to study the extension of this work in order to be generally applicable to any P2P data management system.

This paper has mainly studied the quantitative gain that could be obtained in query processing from exploiting data summaries. Future works aim to implement this technique on real data (not synthetic) in order to study the qualitative gain that can be obtained from approximately answering a query.

The novelty of the approach shown in the paper relies on the double exploitation of the summaries in P2P systems: data summaries allow for a semantic‐based query routing, and also for an approximate query answering, using their intentional descriptions.

In response to the urgent national need to implement evidence-based literacy supports for adolescent struggling readers (ASRs), this chapter provides a framework for addressing reading comprehension instruction. Schools face significant challenges in the education of ASRs including how to address the achievement gap that emerges between proficient readers and a variety of poor reader subgroups predicted by the Simple View of Reading. The authors present current research in the components of reading comprehension (e.g., text structures, vocabulary, prior knowledge, cognitive strategies, and motivation) and explicit pedagogical practices associated with improving outcomes for ASRs, including a school-wide framework called the Content Literacy Continuum. Two specific interventions with supporting research are presented as model practices to improve outcomes for ASRs.

This paper ‘looks” into one of the most novel knowledge management technology products that has been brought to the market in the recent years. The authors describe two technologies, information visualization and knowledge extraction, for leveraging our natural abilities of vision, language and memory. They discuss a way for exploiting structure that is available in the information system in one case (traditionally called structured) and easily perceived by humans in the other (traditionally called unstructured). The two technologies focus on the two sides of this goal, respectively. They demonstrate the value of these technologies in supporting interaction with much larger amounts of information than was possible with previous graphical interfaces and in guiding access and use of the information and often for automating portions of the work.

The purpose of this paper is to argue for the reflective use of visual techniques in qualitative inter-viewing and suggests using visuals not only as projective techniques to elicit answers, but also as facilitation techniques throughout the interview process.

By reflecting on their own research projects in organization and management studies, the authors develop a practical approach to visual interviewing – making use of both projective and facilitation techniques. The paper concludes by discussing the limitations of visualization techniques, and suggesting directions for future research on visually enhanced interviewing.

The integration of projective and facilitation techniques enables the interviewer to build rapport with the respondent(s), and to elicit deeper answers by providing cognitive stimulation. In the course of the interview, such an integrative approach brings along further advantages, most notably focusing attention, maintaining interaction, and fostering the co-construction of knowledge between the interviewer and the interviewee(s).

This paper is reflective of what is currently occurring in the field of qualitative interviewing, and presents a practical approach for the integration of visual projection and facilitation in qualitative interviews.

Keywords search is intuitive, simple to use and convenient. It is also the de facto input interface for textual and multimedia retrieval. However, individuals often perform poorly when faced with exploratory search tasks that are common during learning, resulting in poor quality searches. The purpose of this paper is to examine how adolescent learners search and select videos to support self-learning. The findings allow for the identification of design concepts of video retrieval interface and features that can facilitate better exploratory searches.

Participants were assigned two customized video search tasks. The think-aloud protocol is used to allow participants to verbalize their actions, thoughts and feeling. This approach offered rich insights to the participants’ cognitive processes and considerations when performing the search tasks.

This study identified five themes for exploratory video search behavior: selection of internet resources, query formulation/reformulation, selection of the video(s) for preview, getting acquainted with the video content, and making a decision for the search task. The analysis of these themes led to a number of design concepts, ranging from supporting exploration of topics to better interaction with metadata.

The findings can inform future development of dedicated video retrieval systems interfaces that seeks to facilitate effective exploratory searches by learners.

This study contributes by suggesting design concepts for video retrieval system developers to support exploratory video searches.

We propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.

Desirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.

Financial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.

We had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.