Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Recurrent Neural Networks with Mixed Hierarchical Structures for Natural Language Processing
Hierarchical structures exist in both linguistics and Natural Language Processing (NLP) tasks. How to design RNNs to learn hierarchical representations of natural languages remains a long-standing challenge. In this pape…
Document ClassificationSentenceGame-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index
The input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with the embedding layer o…
Document ClassificationNatural Language InferenceQuestion AnsweringSentenceLightweight Cross-Lingual Sentence Representation Learning
Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increa…
Contrastive LearningDocument ClassificationLanguage ModelingLanguage Modelling+4SciFive: a text-to-text transformer model for biomedical literature
In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora. Our model outperforms the current SOTA methods (i.e. BERT, BioBERT, Base T5) on tasks in named entit…
Document ClassificationDrug–drug Interaction ExtractionNamed Entity Recognition (NER)Natural Language Inference+4Towards Bengali Word Embedding: Corpus Creation, Intrinsic and Extrinsic Evaluations
Distributional word vector representation or word embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval a…
Document ClassificationInformation RetrievalMachine TranslationQuestion Answering+5Three-level Hierarchical Transformer Networks for Long-sequence and Multiple Clinical Documents Classification
We present a Three-level Hierarchical Transformer Network (3-level-HTN) for modeling long-term dependencies across clinical notes for the purpose of patient-level prediction. The network is equipped with three levels of …
Document ClassificationGeneral ClassificationSentenceTemporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media
Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve perform…
Document ClassificationDomain AdaptationGeneral ClassificationLanguage ModellingAn Analysis of a BERT Deep Learning Strategy on a Technology Assisted Review Task
Document screening is a central task within Evidenced Based Medicine, which is a clinical discipline that supplements scientific proof to back medical decisions. Given the recent advances in DL (Deep Learning) methods ap…
ArticlesDocument ClassificationInformation RetrievalRetrievalDeveloping Annotated Resources for Internal Displacement Monitoring
This paper describes in details the design and development of a novel annotation framework and of annotated resources for Internal Displacement, as the outcome of a collaboration with the Internal Displacement Monitoring…
Document ClassificationNot All Attention Is All You Need
Beyond the success story of pre-trained language models (PrLMs) in recent natural language processing, they are susceptible to over-fitting due to unusual large model size. To this end, dropout serves as a therapy. Howev…
AllDocument ClassificationNamed Entity Recognition (NER)Natural Language UnderstandingExploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification
Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concern…
ClassificationDocument ClassificationGeneral ClassificationSentiment Analysis+2GEPSA, a tool for monitoring social challenges in digital press
This papers presents a platform for monitoring press narratives with respect to several social challenges, including gender equality, migrations and minority languages. As narratives are encoded in natural language, we h…
ClassificationData VisualizationDocument ClassificationKeyword Extraction+4Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-supervised learning algorithms used for image …
Contrastive LearningData AugmentationDocument ClassificationDocument Embedding+3Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework
Acute respiratory distress syndrome (ARDS) is a life-threatening condition that is often undiagnosed or diagnosed late. ARDS is especially prominent in those infected with COVID-19. We explore the automatic identificatio…
Document ClassificationSentencetext-classificationText ClassificationImproving Document-Level Sentiment Classification Using Importance of Sentences
Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that suppor…
ClassificationDocument ClassificationGeneral ClassificationSentence+4Supervised Tree-Wasserstein Distance
To measure the similarity of documents, the Wasserstein distance is a powerful tool, but it requires a high computational cost. Recently, for fast computation of the Wasserstein distance, methods for approximating the Wa…
Document ClassificationGPUMetric LearningMultilingual and cross-lingual document classification: A meta-learning approach
The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited…
Cross-Lingual Document ClassificationDocument ClassificationGeneral ClassificationMeta-LearningCombining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolatio…
ClassificationDocument ClassificationGeneral ClassificationMedical Segment Coloring of Clinical Notes
This paper proposes a deep learning-based method to identify the segments of a clinical note corresponding to ICD-9 broad categories which are further color-coded with respect to 17 ICD-9 categories. The proposed Medical…
Document ClassificationEntropy Partial Transport with Tree Metrics: Theory and Practice
Optimal transport (OT) theory provides powerful tools to compare probability measures. However, OT is limited to nonnegative measures having the same mass, and suffers serious drawbacks about its computation and statisti…
Document ClassificationTopological Data Analysis