2,459 research outputs found
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Personalization, Cognition, and Gamification-based Programming Language Learning: A State-of-the-Art Systematic Literature Review
Programming courses in computing science are important because they are often
the first introduction to computer programming for many students. Many
university students are overwhelmed with the information they must learn for an
introductory course. The current teacher-lecturer model of learning commonly
employed in university lecture halls often results in a lack of motivation and
participation in learning. Personalized gamification is a pedagogical approach
that combines gamification and personalized learning to motivate and engage
students while addressing individual differences in learning. This approach
integrates gamification and personalized learning strategies to inspire and
involve students while addressing their unique learning needs and differences.
A comprehensive literature search was conducted by including 81 studies that
were analyzed based on their research design, intervention, outcome measures,
and quality assessment. The findings suggest that personalized gamification can
enhance student cognition in programming courses by improving motivation,
engagement, and learning outcomes. However, the effectiveness of personalized
gamification varies depending on various factors, such as the type of
gamification elements used, the degree of personalization, and the
characteristics of the learners. This paper provides insights into designing
and implementing effective personalized gamification interventions in
programming courses. The findings could inform educational practitioners and
researchers in programming education about the potential benefits of
personalized gamification and its implications for educational practice
A Survey on Conversational Search and Applications in Biomedicine
This paper aims to provide a radical rundown on Conversation Search
(ConvSearch), an approach to enhance the information retrieval method where
users engage in a dialogue for the information-seeking tasks. In this survey,
we predominantly focused on the human interactive characteristics of the
ConvSearch systems, highlighting the operations of the action modules, likely
the Retrieval system, Question-Answering, and Recommender system. We labeled
various ConvSearch research problems in knowledge bases, natural language
processing, and dialogue management systems along with the action modules. We
further categorized the framework to ConvSearch and the application is directed
toward biomedical and healthcare fields for the utilization of clinical social
technology. Finally, we conclude by talking through the challenges and issues
of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an
integrated and unified vision of the ConvSearch components from different
fields, which benefit the information-seeking process in healthcare systems
A reinforcement learning formulation to the complex question answering problem
International audienceWe use extractive multi-document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem. Given a set of complex questions, a list of relevant documents per question, and the corresponding human generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to previously unseen complex questions. A reward function is used to measure the similarities between the candidate (machine generated) summary sentences and the abstract summaries. In the training stage, the learner iteratively selects the important document sentences to be included in the candidate summary, analyzes the reward function and updates the related feature weights accordingly. The final weights are used to generate summaries as answers to unseen complex questions in the testing stage. Evaluation results show the effectiveness of our system. We also incorporate user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experiments reveal the positive impact of the user interaction component on the reinforcement learning framework
Personalized Memory Transfer for Conversational Recommendation Systems
Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach
QueRIE: Collaborative Database Exploration
Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach
NLP-based personal learning assistant for school education
Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future
A Review of Question Answering Systems: Approaches, Challenges, and Applications
Question answering (QA) systems are a type of natural language processing (NLP) technology that provide precise and concise answers to questions posed in natural language. These systems have the potential to revolutionize the way we access information and can be applied in a wide range of fields including education, customer service, and health care.There are several approaches to building QA systems, including rule-based, information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and patterns to extract answers from a given text, while information retrieval systems use search algorithms to retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need to understand the context and intent behind a question. This requires the system to have a deep understanding of the language and the ability to make inferences based on the given information. Another challenge is the need to extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA systems have a wide range of applications, including education, customer service, and health care. In education, QA systems can be used to provide personalized learning experiences and help students learn more efficiently. In customer service, QA systems can be used to handle a high volume of queries and provide quick and accurate responses to customers. In health care, QA systems can be used to assist doctors and patients by providing timely and accurate information about medical conditions and treatments.Overall, this review aims to provide a comprehensive overview of QA systems, their approaches, challenges, and applications. By understanding the current state of development and the potential impact of QA systems, we can better utilize these technologies to improve various industries and enhance the way we access information
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