20,267 research outputs found
Thumbs up? Sentiment Classification using Machine Learning Techniques
We consider the problem of classifying documents not by topic, but by overall
sentiment, e.g., determining whether a review is positive or negative. Using
movie reviews as data, we find that standard machine learning techniques
definitively outperform human-produced baselines. However, the three machine
learning methods we employed (Naive Bayes, maximum entropy classification, and
support vector machines) do not perform as well on sentiment classification as
on traditional topic-based categorization. We conclude by examining factors
that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200
Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)
"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.
Text Classification: A Review, Empirical, and Experimental Evaluation
The explosive and widespread growth of data necessitates the use of text
classification to extract crucial information from vast amounts of data.
Consequently, there has been a surge of research in both classical and deep
learning text classification methods. Despite the numerous methods proposed in
the literature, there is still a pressing need for a comprehensive and
up-to-date survey. Existing survey papers categorize algorithms for text
classification into broad classes, which can lead to the misclassification of
unrelated algorithms and incorrect assessments of their qualities and behaviors
using the same metrics. To address these limitations, our paper introduces a
novel methodological taxonomy that classifies algorithms hierarchically into
fine-grained classes and specific techniques. The taxonomy includes methodology
categories, methodology techniques, and methodology sub-techniques. Our study
is the first survey to utilize this methodological taxonomy for classifying
algorithms for text classification. Furthermore, our study also conducts
empirical evaluation and experimental comparisons and rankings of different
algorithms that employ the same specific sub-technique, different
sub-techniques within the same technique, different techniques within the same
category, and categorie
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves
in the field of natural language processing and artificial intelligence, due to
their emergent ability and generalizability. However, LLMs are black-box
models, which often fall short of capturing and accessing factual knowledge. In
contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are
structured knowledge models that explicitly store rich factual knowledge. KGs
can enhance LLMs by providing external knowledge for inference and
interpretability. Meanwhile, KGs are difficult to construct and evolving by
nature, which challenges the existing methods in KGs to generate new facts and
represent unseen knowledge. Therefore, it is complementary to unify LLMs and
KGs together and simultaneously leverage their advantages. In this article, we
present a forward-looking roadmap for the unification of LLMs and KGs. Our
roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs,
which incorporate KGs during the pre-training and inference phases of LLMs, or
for the purpose of enhancing understanding of the knowledge learned by LLMs; 2)
LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding,
completion, construction, graph-to-text generation, and question answering; and
3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a
mutually beneficial way to enhance both LLMs and KGs for bidirectional
reasoning driven by both data and knowledge. We review and summarize existing
efforts within these three frameworks in our roadmap and pinpoint their future
research directions.Comment: 29 pages, 25 figure
Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education
This paper presents a novel framework, Artificial Intelligence-Enabled
Intelligent Assistant (AIIA), for personalized and adaptive learning in higher
education. The AIIA system leverages advanced AI and Natural Language
Processing (NLP) techniques to create an interactive and engaging learning
platform. This platform is engineered to reduce cognitive load on learners by
providing easy access to information, facilitating knowledge assessment, and
delivering personalized learning support tailored to individual needs and
learning styles. The AIIA's capabilities include understanding and responding
to student inquiries, generating quizzes and flashcards, and offering
personalized learning pathways. The research findings have the potential to
significantly impact the design, implementation, and evaluation of AI-enabled
Virtual Teaching Assistants (VTAs) in higher education, informing the
development of innovative educational tools that can enhance student learning
outcomes, engagement, and satisfaction. The paper presents the methodology,
system architecture, intelligent services, and integration with Learning
Management Systems (LMSs) while discussing the challenges, limitations, and
future directions for the development of AI-enabled intelligent assistants in
education.Comment: 29 pages, 10 figures, 9659 word
New lenses to investigate media use: The layering process perspective
Email has been recognized as one of the chief sources of the current epidemic of information
overload. The work described in this paper is part of an ongoing project to re-focus the attention of
information workers on “what needs to be done” rather than “what someone wants us to read”. The
approach is to base a software agent that pre-processes incoming email on the user’s “personal
ontology”. The ontology used in this work contained two facets, namely Work Topics and Task Types,
and also included lexical “clues” whereby, if such a word or phrase is found in the text of a message,
the system can infer with a certain probability that a particular ontology concept (class or instance) is
relevant. The paper describes the prototype tools developed and some initial trials. Further trials and
improvements are planned
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