Meaning-sensitive noisy text analytics in the low data regime

Digital connectivity is revolutionising people’s quality of life. As broadband and mobile services become faster and more prevalent globally than before, people have started to frequently express their wants and desires on social media platforms. Thus, deriving insights from text data has become a popular approach, both in the industry and academia, to provide social media analytics solutions across a range of disciplines, including consumer behaviour, sales, sports and sociology. Businesses can harness the data shared on social networks to improve their organisations’ strategic business decisions by leveraging advanced Natural Language Processing (NLP) techniques, such as context-aware representations. Specifically, SportsHosts, our industry partner, will be able to launch digital marketing solutions that optimise audience targeting and personalisation using NLP-powered solutions. However, social media data are often noisy and diverse, making the task very challenging. Further, real-world NLP tasks often suffer from insufficient labelled data due to the costly and time-consuming nature of manual annotation. Nevertheless, businesses are keen on maximising the return on investment by boosting the performance of these NLP models in the real world, particularly with social media data. In this thesis, we make several contributions to address these challenges. Firstly, we propose to improve the NLP model’s ability to comprehend noisy text in a low data regime by leveraging prior knowledge from pre-trained language models. Secondly, we analyse the impact of text augmentation and the quality of synthetic sentences in a context-aware NLP setting and propose a meaning-sensitive text augmentation technique using a Masked Language Model. Thirdly, we offer a cost-efficient text data annotation methodology and an end-to-end framework to deploy efficient and effective social media analytics solutions in the real world.Doctor of Philosoph

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

Normalization of common noisy terms in Malaysian online media

This paper proposes a normalization technique of noisy terms that occur in Malaysian micro-texts.Noisy terms are common in online messages and influence the results of activities such as text classification and information retrieval.Even though many researchers have study methods to solve this problem, few had looked into the problems using a language other than English. In this study, about 5000 noisy texts were extracted from 15000 documents that were created by the Malaysian.Normalization process was executed using specific translation rules as part or preprocessing steps in opinion mining of movie reviews.The result shows up to 5% improvement in accuracy values of opinion mining

Neurons and Symbols: A Manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

Noise-tolerance feasibility for restricted-domain Information Retrieval systems

Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented

Dagstuhl Seminar Proceedings 10302 Learning paradigms in dynamic environments

Abstract We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty. Overview The study of human behaviour is an important part of computer science, artificial intelligence (AI), neural computation, cognitive science, philosophy, psychology and other areas. Among the most prominent tools in the modelling of behaviour are computational-logic systems (classical logic, nonmonotonic logic, modal and temporal logic) and connectionist models of cognition (feedforward and recurrent networks, symmetric and deep networks, self-organising networks). Recent studies in cognitive science, artificial intelligence and evolutionary psychology have produced a number of cognitive models of reasoning, learning and language that are underpinned by computatio