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Search Engine Opt imisation, also known as SEO, is one of the online marketing channels t hat, when it is s et u p s uitably, it could continue to pay dividends over time without investment. Recently, SEO teams of some companies keep investigating historical data to predict the future trends of revenue associated with the number of clicks, number of imp ressions, and number of sear ches which is prop osed to help with companies’ quality planning and campaign investment. Some of the challenges experienced by SEO analysts when attempting to forecast the revenue is that there is currently no way to standardize or forecast customers’ behaviour, which means the trends could be different every day, month, and year. In this research, SEO traffic data from one of the online travel a gencies are collected for the purpose of data exploration, analysis and forecasting which are expected to bring business values and give some be neficialinsights. Moreover, different time-series forecasting models are selected to conduct experiments seeking the best fit model for SEO data; Autoregressive Integrated Moving Average (ARIMA) model is initially performed, followed by Long-Short Term Memory (LSTM) of Recurrent Neural Networks (RNNs). As a result, it is proved that ARIMA is yet a classical statistics model but powerful enough for such small-size data, albeit the data is non-stationary and has too much white noise. Me anwhile, the LSTM is a deep learning tool which could deal with different types of data, but still need to be applied with a larger size of data to prove its competence.
Hypothesis Extraction from Academic Papers Using Neural Networks for Ontology Theory Learning
(2019)
In this study, we investigated a new use case of deep learning. We applied deep learning to extract causes and effects from the hypotheses of the scientific papers. The research presents a variety of RNN models, including RNN models with CRF layer for labelling the sequences. We used such models as Bi-LSTM, LSTM, SimpleRNN and GRU. The experiments were conducted with GloVe vector representation and character level vector representation of words. Moreover, along with RNN models, we evaluated various hyperparameters and model setups to achieve the highest performance scores. In the end, we obtained promising results and shared our thoughts on the future prospects of the following studies.
This thesis examines the effectiveness of the latest Transfer Learning techniques for Natural Language Processing applied to the classification of research methods used in scientific journals in the domain of Information Systems. The task of automated knowledge extraction from academic articles has seen ongoing progress in recent years. However, the combination of transfer and Deep Learning in order to assign research methods to scientific papers has not been addressed in the literature yet. The main contribution of this thesis is, therefore, an artifact that applies cutting-edge Transfer Learning techniques to a Deep Learning model by conducting several experiments and comparing their effectiveness. The prototype considers various ways of fine-tuning that are crucial to retain the knowledge transferred from pretrained models and avoid catastrophic forgetting. Additionally, this work discusses the literature with regard to the task-specific Theory Ontology Learning and the method-specific state of the art in Transfer Learning for Natural Language Processing. As a result, the artifact surpassed the performance of previously developed models for research method extraction, presented in the literature, without applying any custom feature engineering and only using around a thousand of labeled observations.
This research paper explores the application of the Universal Language Model Fine-tuning (ULMFiT) technique, a novel deep transfer learning approach in the Natural Language Processing (NLP) field, to the financial statements fraud detection task. Additionally, the artifact investigated a simpler model, represented by a one-dimensional Convolutional Neural Network (CNN). Both methods have been assessed with respect to the training time and predefined evaluation metrics. Overall, ULMFiT turned out to be considerably more computationally expensive to train and achieved an accuracy of 77% with F-measure of 13%, if used with a decision boundary of 0.5, and accuracy of 59% with F-measure of 42%, if calculated with a threshold of 0.2. In contrast, CNN model trained significantly faster and obtained the following metrics: accuracy of 82% with F-measure of 42% for threshold 0.5, and accuracy of 83% with F-measure of 58% for threshold 0.2. As a result, ULMFiT has been outperformed by one-dimensional CNN on all examined metrics. The results are reported by using two different values for decision boundary due to the precision-recall trade-off, depending on the use case. In addition, this thesis investigated the impact of data preprocessing. The findings have shown that removing all numbers and special symbols with supplementary text truncation, limiting the sequence length, had a positive effect on both models, mentioned above.
Deep Learning, a topic of broad and current interest, has undergone rapid development in the last decade. The performance of the algorithms is already above human level. However, the area of Natural Language Processing is still a great challenge for the researchers. This study aims at exploring the novel ULMFiT method for text classification by applying it to a dataset of scientific articles with advanced rhetorical categories. This classification task is challenging even for humans and it requires a substantial analysis of the texts when conducted by machine learning algorithms. The objective of this attempt is to achieve text classification with minimal preparation. The ULMFiT method is the first successful effort to apply transfer learning to NLP tasks. Its performance will be evaluated on a task that requires a level of understanding beyond the semantic meaning of the text.