Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the classification model. Traditional machine learning methods represent documents in vector space using features such as term frequencies, which have limitations in handling the order and semantics of words. Meanwhile, although achieving many successes, deep learning classifiers require substantial resources in terms of labelled data and computational complexity. In this work, a weighted ensemble of classifiers (WEC) is introduced to address the text classification problem. Instead of using majority vote as the combining method, we propose to associate each classifier's prediction with a different weight when combining classifiers. The optimal weights are obtained by minimising a loss function on the training data with the Particle Swarm Optimisation algorithm. We conducted experiments on 5 popular datasets and report classification performance of algorithms with classification accuracy and macro F1 score. WEC was run with several different combinations of traditional machine learning and deep learning classifiers to show its flexibility and robustness. Experimental results confirm the advantage of WEC, especially on smaller datasets.
Automated generation of human readable text from structured information is challenging because grammatical rules are complex making good quality outputs difficult to achieve. Textual Case-Based Reasoning provides one approach in which the text from previously solved examples with similar inputs is reused as a template solution to generate text for the current problem. Natural Language Generation also poses a challenge when evaluating the quality of the text generated due to the high cost of human labelling and the variety in potential good quality solutions. In this paper, we propose two case-based approaches for reusing text to automatically generate an obituary from a set of input attribute-value pairs. The case-base is acquired by crawling and then tagging existing solutions published on the web to create cases as problem-solution pairs. We evaluate the quality of the text generation system with a novel unsupervised case alignment metric using normalised discounted cumulative gain which is compared to a supervised approach and human evaluation. Initial results show that our proposed evaluation measure is effective and correlates well with average attribute error evaluation which is a crude surrogate to human feedback. The system is being deployed in a real-world application with a startup company in Aberdeen to produce automated obituaries.
Traditional Data-to-Text Generation (D2T) systems utilise carefully crafted domain specific rules and templates to generate high quality accurate texts. More recent approaches use neural systems to learn domain rules from the training data to produce very fluent and diverse texts. However, there is a trade-off with rule-based systems producing accurate text but that may lack variation, while learning-based systems produce more diverse texts but often with poorer accuracy. In this paper, we propose a Case-Based approach for D2T that mitigates the impact of this trade-off by dynamically selecting templates from the training corpora. In our approach we develop a novel case-alignment based, feature weighing method that is used to build an effective similarity measure. Extensive experimentation is performed on a sports domain dataset. Through Extractive Evaluation metrics, we demonstrate the benefit of the CBR system over a rule-based baseline and a neural benchmark.
Research on wireless sensor networks has recently received much attention as they offer an advantage of monitoring various kinds of environment by sensing physical phenomenon. Prolonged network lifetime, scalability, and load balancing are important requirement for many sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. The primary objectives of the wireless sensor network routing protocol design are balancing network energy consumption and extending the entire network lifetime. Clustering is an effective technique that can greatly contribute to overall system scalability, lifetime, and energy efficiency in wireless sensor networks (WSNs). In this paper, we propose an energy efficient clustering algorithm for WSNs based on the LEACH algorithm. LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the most well known energy efficient clustering algorithms for WSNs. The proposed algorithm solves the extra transmissions problem that can occurs in LEACH algorithm.
The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking the generation process into some variant of planning and realisation phases. Traditional methods have been very good at producing high quality texts but are difficult to build for complex domains and also lack diversity. On the other hand, current neural systems offer scalability and diversity but at the expense of being inaccurate. Case-Based approaches try to mitigate the accuracy and diversity trade-off by providing better accuracy than neural systems and better diversity than traditional systems. However, they still fare poorly against neural systems when measured on the dimensions of content selection and diversity. In this work, a Case-Based approach for content-planning in D2T, called CBR-Plan, is proposed which selects and organises the key components required for producing a summary, based on similar previous examples. Extensive experiments are performed to demonstrate the effectiveness of the proposed method against a variety of benchmark and baseline systems, ranging from template-based, to case-based and neural systems. The experimental results indicate that CBR-Plan is able to select more relevant and diverse content than other systems.
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