Text Classification With Deep Neural Networks

The thesis explores different extensions of Deep Neural Networks in learning underlying natural language representations and how to apply them in Natural Language Processing tasks. Novel methods of learning lower or higher level features of natural languages are given in which word and phrase dense representations are derived from unlabelled corpora. Word representations are learned by training Deep Neural Networks to predict context from each sentence while phrase representations are learned by unsupervised learning with Convolutional Restricted Boltzmann Machine. It is shown that word representations learned from architectures which preserve text input as sequences have better word similarity and relatedness than bag-of-word approaches. Additionally phrase representations learned with Convolutional Restricted Boltzmann Machine when combined with bag-of-word features improve results of text classification tasks over only bag-of-word features. Beside learning word and phrase representations, to the best of my knowledge, the work in the thesis is first to explore Deep Neural Networks in Adverse Drug Reaction detection task where my architectures when used with pre-trained word representations significantly outperform the state-of-the-art models. In addition, outputs from my proposed attentional architecture can be used to highlight important word spans without explicit training labels. In the future I propose the learned representations to be used with the discussed Deep Neural Networks in different NLP tasks such as Dialog Systems, Machine Translation or Natural Language Inference

Trust and reputation in open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this thesis develops and evaluates FIRE, a trust and reputation model that enables autonomous agents in open MAS to evaluate the trustworthiness of their peers and to select good partners for interactions. FIRE integrates four sources of trust information under the same framework in order to provide a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation, that models trust resulting from direct experiences, role-based relationships, witness reports, and third-party references, respectively, to provide trust metrics in most circumstances. A novel model of reporter credibility has also been integrated to enable FIRE to effectively deal with inaccurate reports (from witnesses and referees). Finally, adaptive techniques have been introduced, which make use of the information gained from monitoring the environment, to dynamically adjust a number of FIRE’s parameters according to the actual situation an agent finds itself in. In all cases, a systematic empirical analysis is undertaken to evaluate the effectiveness of FIRE in terms of the agent’s performance

BER of high-speed OFDM systems in the presence of offset mismatch of TI-ADCs

Time-interleaved analog-to-digital converters (TI-ADCs) are widely used for multi-Gigabit orthogonal frequency division multiplexing (OFDM) systems because of their attractive high sampling rate and high resolution. However, in practice, offset mismatch, one of the major mismatches of TI-ADCs, can occur between the parallel sub-ADCs. In this poster session, we theoretically analyze the BER performance of high-speed OFDM systems using TI-ADCs with offset mismatch. Gray-coded PAM or QAM signaling over an additive white Gaussian noise channel is considered. Our numerical results show that the obtained theoretical BER expressions are in excellent agreement with the simulated BER performance

Adverse Drug Reaction Classification With Deep Neural Networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs

BER analysis of high-speed OFDM systems in the presence of time-interleaved analog-to-digital converter's offset mismatch

Time-interleaved analog-to-digital converters (TI-ADCs) are widely used for multi-Gigabit orthogonal frequency division multiplexing (OFDM) systems because of their attractive high sampling rate and high resolution. However, mismatch between the parallel sub-ADCs can severely degrade the system performance. Several types of mismatch can be distinguished, one particular kind of mismatch is offset mismatch, which originates from the different DC offsets in the different sub-ADCs. Although some authors have studied the effect of offset mismatch on the bit error rate (BER) performance, exact close-form BER expressions in the presence of offset mismatch have not been derived yet. In this poster, we derive such BER expressions. Gray-coded PAM or QAM signaling over an additive white Gaussian noise channel is considered. Our numerical results show that the obtained theoretical BER expressions are in excellent agreement with the simulated BER performance. We also investigate simplified expressions for the error floor occurring at large SNR and large offset mismatch. Our finding shows that this error floor is essentially independent of the modulation order and the type of modulation