There is a critical need to develop a mobile-based emergency response system (MERS) to help reduce risks in emergency situations. Existing systems only provide short message service (SMS) notifications, and the decision support is weak, especially in man-made disaster situations. This paper presents a MERS ontology-supported case-based reasoning (OS-CBR) method, with implementation, to support emergency decision makers to effectively respond to emergencies. The advantages of the OS-CBR approach is that it builds a case retrieving process, which provides a more convenient system for decision support based on knowledge from, and solutions provided for past disaster events. The OS-CBR approach includes a set of algorithms that have been successfully implemented in four components: data acquisition; ontology; knowledge base; and reasoning; as a sub-system of the MERS framework. A set of experiments and case studies validated the OS-CBR approach and application, and demonstrate its efficiency.

Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services

Revealing the theoretical basis of gamification: A systematic review and analysis of theory in research on gamification, serious games and game-based learning

Chatbots have been around for years and have been used in many areas such as medicine or commerce. Our focus is on the development and current uses of chatbots in the field of education, where they can function as service assistants or as educational agents. In this research paper, we attempt to make a systematic review of the literature on educational chatbots that address various issues. From 485 sources, 80 studies on chatbots and their application in education were selected through a step‐by‐step procedure based on the guidelines of the PRISMA framework, using a set of predefined criteria. The results obtained demonstrate the existence of different types of educational chatbots currently in use that affect student learning or improve services in various areas. This paper also examines the type of technology used to unravel the learning outcome that can be obtained from each type of chatbots. Finally, our results identify instances where a chatbot can assist in learning under conditions similar to those of a human tutor, while exploring other possibilities and techniques for assessing the quality of chatbots. Our analysis details these findings and can provide a solid framework for research and development of chatbots for the educational field.

Rediscovering the use of chatbots in education: A systematic literature review

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches. However, these traditional approaches are heavily reliant on large-scale dictionaries, target-specific rules, or well-constructed corpora. These methods to NER have been superseded by the deep learning-based approach that is independent of hand-crafted features. However, although such methods of NER employ additional conditional random fields (CRF) to capture important correlations between neighboring labels, they often do not incorporate all the contextual information from text into the deep learning layers. We propose herein an NER system for biomedical entities by incorporating n-grams with bi-directional long short-term memory (BiLSTM) and CRF; this system is referred to as a contextual long short-term memory networks with CRF (CLSTM). We assess the CLSTM model on three corpora: the disease corpus of the National Center for Biotechnology Information (NCBI), the BioCreative II Gene Mention corpus (GM), and the BioCreative V Chemical Disease Relation corpus (CDR). Our framework was compared with several deep learning approaches, such as BiLSTM, BiLSTM with CRF, GRAM-CNN, and BERT. On the NCBI corpus, our model recorded an F-score of 85.68% for the NER of diseases, showing an improvement of 1.50% over previous methods. Moreover, although BERT used transfer learning by incorporating more than 2.5 billion words, our system showed similar performance with BERT with an F-scores of 81.44% for gene NER on the GM corpus and a outperformed F-score of 86.44% for the NER of chemicals and diseases on the CDR corpus. We conclude that our method significantly improves performance on biomedical NER tasks. The proposed approach is robust in recognizing biological entities in text.

/pdf/biomedical-named-entity-recognition-using-deep-neural-3tgxy9t1wh.pdf

Biomedical named entity recognition using deep neural networks with contextual information

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelli-gence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, in-depth research and analysis of op-timal settings of the various components of Seq2Seq model for natural an-swer generation problem is very limited. Additionally, there has been no ex-periments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experi-ments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a cu-rated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model.

/pdf/sequence-to-sequence-model-performance-for-education-chatbot-5glkdyehft.pdf

Sequence to Sequence Model Performance for Education Chatbot

Information retrieval involves finding some required information in a collection of information or in databases. The information or database need not necessarily be in one language. In other words, language should not limit the finding of information. The way to search for the information is by looking at every item in the collection and when the need to translate the language arises, the techniques and methods developed for the cross-lingual retrieval system is used. This paper reviews some recent researches focusing on topics in cross-lingual information retrieval and their role in current research directions which include new models and paradigms in the wide area of information retrieval.

Cross-lingual Information Retrieval State-of-the-Art

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefined mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction.

Semantics representation in a sentence with concept relational model (CRM)

Sentiment analysis finds opinions, sentiments or emotions in user-generated contents. Most efforts are focusing on the English language, for which a large amount of sources and tools for sentiment analysis are available. The objective of this paper is to introduce a cross-lingual sentiment lexicon acquisition method for the Malay and English languages and further being test on a set of news test collections. Several part of speech tags are being experimented using the Word Score Summation technique in order to classify the sentiment of the news articles. This method records up to 50% as experimental accuracy result and works better for verbs and negations in both the English and Malay news articles.

/pdf/english-and-malay-cross-lingual-sentiment-lexicon-4caicoqwko.pdf

English and Malay Cross-lingual Sentiment Lexicon Acquisition and Analysis

Information retrieval involves finding some required information in a collection of information or in database The collection not necessarily be in one language only as information does not limited to language The simplest way to search for the information is to look at every item in the collection and when the need to translate the languages being used arises, this is where the techniques and methods that were being developed for the cross-lingual retrieval system will take place This article reviews some recent researches focusing on topics in cross-lingual information retrieval and their role in current research directions in the wide area of information retrieval

/pdf/a-review-on-the-cross-lingual-information-retrieval-4vw9ie2q3s.pdf

Nurul Amelina Nasharuddin

Papers

Sequence to Sequence Model Performance for Education Chatbot

Cross-lingual Information Retrieval State-of-the-Art

Semantics representation in a sentence with concept relational model (CRM)

English and Malay Cross-lingual Sentiment Lexicon Acquisition and Analysis

A review on the cross-lingual information retrieval