3,209 research outputs found
CCheXR-Attention: Clinical concept extraction and chest x-ray reports classification using modified Mogrifier and bidirectional LSTM with multihead attention
Radiology reports cover different aspects, from radiological observation to the diagnosis of an imaging examination, such as X-rays, MRI, and CT scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free-text reporting format, which causes the loss of a large amount of information in unstructured text. Second, the extraction of important features from these reports is a huge bottleneck for machine learning models. These challenges are important, particularly the extraction of key features such as symptoms, comparison/priors, technique, finding, and impression because they facilitate the decision-making on patients’ health. To alleviate this issue, a novel architecture CCheXR-Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified mogrifier LSTM model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state-of-the-art models
Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches
Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people.
Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy.
The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech.
In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection
Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers
With the advent of the modern pre-trained Transformers, the text preprocessing has started to be neglected and not specifically addressed in recent NLP literature. However, both from a linguistic and from a computer science point of view, we believe that even when using modern Transformers, text preprocessing can significantly impact on the performance of a classification model. We want to investigate and compare, through this study, how preprocessing impacts on the Text Classification (TC) performance of modern and traditional classification models. We report and discuss the preprocessing techniques found in the literature and their most recent variants or applications to address TC tasks in different domains. In order to assess how much the preprocessing affects classification performance, we apply the three top referenced preprocessing techniques (alone or in combination) to four publicly available datasets from different domains. Then, nine machine learning models – including modern Transformers – get the preprocessed text as input. The results presented show that an educated choice on the text preprocessing strategy to employ should be based on the task as well as on the model considered. Outcomes in this survey show that choosing the best preprocessing technique – in place of the worst – can significantly improve accuracy on the classification (up to 25%, as in the case of an XLNet on the IMDB dataset). In some cases, by means of a suitable preprocessing strategy, even a simple Naïve Bayes classifier proved to outperform (i.e., by 2% in accuracy) the best performing Transformer. We found that Transformers and traditional models exhibit a higher impact of the preprocessing on the TC performance. Our main findings are: (1) also on modern pre-trained language models, preprocessing can affect performance, depending on the datasets and on the preprocessing technique or combination of techniques used, (2) in some cases, using a proper preprocessing strategy, simple models can outperform Transformers on TC tasks, (3) similar classes of models exhibit similar level of sensitivity to text preprocessing
Graph embedding approach to analyze sentiments on cryptocurrency
This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data
Improving Cross-Lingual Transfer Learning for Event Detection
The widespread adoption of applications powered by Artificial Intelligence (AI) backbones has unquestionably changed the way we interact with the world around us. Applications such as automated personal assistants, automatic question answering, and machine-based translation systems have become mainstays of modern culture thanks to the recent considerable advances in Natural Language Processing (NLP) research. Nonetheless, with over 7000 spoken languages in the world, there still remain a considerable number of marginalized communities that are unable to benefit from these technological advancements largely due to the language they speak. Cross-Lingual Learning (CLL) looks to address this issue by transferring the knowledge acquired from a popular, high-resource source language (e.g., English, Chinese, or Spanish) to a less favored, lower-resourced target language (e.g., Urdu or Swahili). This dissertation leverages the Event Detection (ED) sub-task of Information Extraction (IE) as a testbed and presents three novel approaches that improve cross-lingual transfer learning from distinct perspectives: (1) direct knowledge transfer, (2) hybrid knowledge transfer, and (3) few-shot learning
Dataflow Programming and Acceleration of Computationally-Intensive Algorithms
The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping
Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification
In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses
Lip2Speech : lightweight multi-speaker speech reconstruction with Gabor features
In environments characterised by noise or the absence of audio signals, visual cues, notably facial and lip movements, serve as valuable substitutes for missing or corrupted speech signals. In these scenarios, speech reconstruction can potentially generate speech from visual data. Recent advancements in this domain have predominantly relied on end-to-end deep learning models, like Convolutional Neural Networks (CNN) or Generative Adversarial Networks (GAN). However, these models are encumbered by their intricate and opaque architectures, coupled with their lack of speaker independence. Consequently, achieving multi-speaker speech reconstruction without supplementary information is challenging. This research introduces an innovative Gabor-based speech reconstruction system tailored for lightweight and efficient multi-speaker speech restoration. Using our Gabor feature extraction technique, we propose two novel models: GaborCNN2Speech and GaborFea2Speech. These models employ a rapid Gabor feature extraction method to derive lowdimensional mouth region features, encompassing filtered Gabor mouth images and low-dimensional Gabor features as visual inputs. An encoded spectrogram serves as the audio target, and a Long Short-Term Memory (LSTM)-based model is harnessed to generate coherent speech output. Through comprehensive experiments conducted on the GRID corpus, our proposed Gabor-based models have showcased superior performance in sentence and vocabulary reconstruction when compared to traditional end-to-end CNN models. These models stand out for their lightweight design and rapid processing capabilities. Notably, the GaborFea2Speech model presented in this study achieves robust multi-speaker speech reconstruction without necessitating supplementary information, thereby marking a significant milestone in the field of speech reconstruction
Location Reference Recognition from Texts: A Survey and Comparison
A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs
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