26 research outputs found

    Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach

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    The exponential increase of available documents online makes document classification an important application in natural language processing. The goal of text classification is to automatically assign categories to documents. Traditional text classifiers depend on features, such as, vocabulary and user-specified information which mainly relies on prior knowledge. In contrast, deep learning automatically learns effective features from data instead of adopting human-designed features. In this thesis, we specifically focus on biomedical document classification. Beyond text information from abstract and title, we also consider image and table captions, as well as paragraphs associated with images and tables, which we demonstrate to be an important feature source to our method

    S-KMN: Integrating Semantic Features Learning and Knowledge Mapping Network for Automatic Quiz Question Annotation

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    Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features

    INVESTIGATING IMPROVEMENTS TO MESH INDEXING

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    The MEDLINE database currently comprises an extensive collection of over 35 million citations, with more than 1 million records being added each year [28]. The abundance of information available in the database presents a significant challenge in identifying and locating relevant research articles on a given search topic. This has prompted the development of various techniques and approaches aimed at improving the efficiency and effectiveness of information retrieval from the MEDLINE database. A search engine devoted to the research publications on MEDLINE is called PubMed. MeSH, or Medical Subject Headings, is a restricted vocabulary used by PubMed to categorize each article. Human annotators have been used for decades, which is not only time-consuming but also expensive. Due to its enormously complex hierarchically ordered structure, MeSH indexing is a difficult problem in the machine learning domain. We propose a model which addresses all these challenges. We propose an end-to-end model that takes MeSH description into account and combines it with a Knowledge Enhanced Mask attention model to index new research papers. We also calculated the journal correlation of each MeSH term in the MeSH hierarchy

    Using High Dimensional Computing on Arabic Language Speech to Text Classification

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    High-Dimensional Processing is the idea that mind register illustrations of neural activities which are not immediately related with numbers. The objective of the article is hyper- dimensional computation of data for categorization of text from two distinct speech datasets, namely the Arabic Corpus dataset and the MediaSpeech dataset with four languages (Arabic, Spanish, French, and Turkish). Through the use of an n-gram encoding scheme, hyper dimensional computing is used to conduct the analysis from the prior set of data. Using hyper dimensional computing, the MediaSpeech dataset accomplishes 100% accuracy for all 4-gram to 14-gram encoding schemes, while the Arabic Corpus dataset accomplishes 100% accuracy for 4-gram to 7-gram encoding schemes

    A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

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    Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%

    Penerapan Weighted Word Embedding pada Pengklasifikasian Teks Berbasis Recurrent Neural Network untuk Layanan Pengaduan Perusahaan Transportasi

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    Twitter menjadi salah satu media sosial yang paling sering dan paling populer digunakan oleh perusahaan sebagai penyedia layanan pelanggan perusahaan. Adanya ribuan cuitan yang dapat masuk dalam setiap hari, tentu akan merepotkan operator layanan untuk mengkategorikan jenis berbagai cuitan tersebut, lebih-lebih jika proses pemilahan kategori cuitan harus dilakukan secara manual. Dalam Tugas Akhir ini, kategoriasi cuitan secara otomatis dibangun dan diimplementasi¬kan menggunakan model klasifikasi berbasis recurrent neural network (RNN) yang dikombinasikan dengan model weighted word embedding (WWE). RNN merupakan salah satu jenis jaringan syaraf tiruan yang populer dan banyak digunakan dalam persoalan klasifikasi, sedangkan WWE merupakan metode yang memungkinkan untuk meng-hubungkan kata-kata yang serupa dengan mengukur jarak semantik antara vektor yang disematkan pada kata tersebut dan memberikan bobot yang berbeda pada setiap kata pada suatu kelas tertentu. Implementasi model penggabungan RNN dan WWE diuji coba menggunakan data pengaduan di perusahaan transportasi untuk data cuitan pada tahun 2015-2016. Hasil uji coba menunjukkan bahwa implementasi WWE baik yang menggunakan model FastText (Weighted FastText) maupun model Word2Vec (Weighted Word2Vec) memberikan hasil yang lebih baik dibandingkan dengan hasil kinerja yang menggabungkan RNN dan model word embedding biasa. Dengan menggunakan metode evaluasi berbasis 10-fold cross validation, model gabungan RNN-Weighted FastText dan RNN-Weighted Word2Vec berturut-turut memberikan hasil akurasi sebesar 88,2% dan 87,5%. Di lain pihak, dengan menggunakan metode evaluasi yang sama, model gabungan RNN-FastText dan RNN-Word2Vec memberikan hasil akurasi yang sama sebesar 83,4%

    Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model

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    Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written,  polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like  XLNet) to effectively represent the features needed for processing.  To improve the performace towards emotion or  sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced  dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as  MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well
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