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Showing 1 to 20 of 118 for “"Text classification"”.
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Concept-based text classification
… thesis is to do automatic concept based document classification. Classification or clustering is the process of grouping similar objects together so that they can be effectively retrieved when queried upon. An experimental system that does this concept based document classification is built by a …
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Weakly-supervised text classification
… gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. …
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Improving Text Classification Using Graph-based Methods
Text classification is a fundamental natural language processing task. However, in real-world applications, class distributions are usually skewed, e.g., due to inherent class imbalance. In addition, the task difficulty changes based on the underlying language. When rich morphological structure and …
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Role of semantic indexing for text classification.
The Vector Space Model (VSM) of text representation suffers a number of limitations for text classification. Firstly, the VSM is based on the Bag-Of-Words (BOW) assumption where terms from the indexing vocabulary are treated independently of one another. However, the expressiveness of natural …
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Multi-Domain Text Classification with Adversarial Training
Text classification is one of the fundamental tasks in natural language processing (NLP), which has been studied for decades and various approaches have been proposed. Unfortunately, text classification is a highly domain-dependent task, a subtle shift between training and testing data …
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Semantic text classification for cancer text mining
… researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is …
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Sequential short-text classification with neural networks
… reviews. In particular, we focus on short-text classification, to help authors of systematic reviews locate the desired information. We introduce several algorithms to perform sequential short-text classification, which outperform state-of-the-art algorithms. To facilitate the choice of …
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Few-shot text classification with distributional signatures
We explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging-lexical features highly informative for one task …
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Granular text classification for biomedical natural language processing
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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Improving multi-class text classification with Naive Bayes
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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Arabic Language Processing for Text Classification. Contributions to Arabic Root Extraction Techniques, Building An Arabic Corpus, and to Arabic Text Classification Techniques.
… for computational processing of Arabic texts. Arabic is a complex language and as such requires in depth investigation for analysis and improvement of available automatic processing techniques such as root extraction methods or text classification techniques, and for developing text …
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Arabic Language Processing for Text Classification. Contributions to Arabic Root Extraction Techniques, Building An Arabic Corpus, and to Arabic Text Classification Techniques.
… for computational processing of Arabic texts. Arabic is a complex language and as such requires in depth investigation for analysis and improvement of available automatic processing techniques such as root extraction methods or text classification techniques, and for developing text …
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LLM-powered active learning for cost-effective text classification
… active learning framework for cost-effective text classification, addressing the challenge of potential LLM annotation errors while balancing annotation quality and model accuracy. Our methodology combines human and large language model (LLM) annotations using uncertainty sampling and …
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Support vector machines, N-gram kernels, and text classification
… to a corresponding increase in the amount of textual data available. This increase is found in the number of web pages, the size and complexity of search engines, and massive volumes of email. For any one attempting to sort through or make sense of this data, one of the fundamental tasks is …
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An Evaluation of Text Classification Methods for Literary Study
… are consistent with what are obtained in topic classification, such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this …
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TextGuard: Provable defense against backdoor attacks on text classification
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Performance analysis of text classification algorithms for PubMed articles
… machine learning models. Various strategies for text multiclass classification were considered. One was a Chi-square test for feature selection which identified words relevant to each MeSH label. The second approach used Named Entity Recognition (NER) to extract entities from the unstructured …
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Few-Shot Semi-Supervised Robust Text Classification with MAML
The need for few-shot semi-supervised text classification arises in a variety of applications, including, e.g., recommendation systems classifying textual content such as product descriptions or news articles based on limited amounts of user feedback. In such settings, existing supervised methods …
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