Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Enhanced word embeddings using multi-semantic representation through lexical chains
The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lex…
Document ClassificationSentenceWord EmbeddingsNetwork Clustering for Multi-task Learning
The Multi-Task Learning (MTL) technique has been widely studied by word-wide researchers. The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representa…
ClusteringDocument ClassificationMulti-Task LearningCan a Fruit Fly Learn Word Embeddings?
The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are…
Document ClassificationWord EmbeddingsWord Sense DisambiguationWord SimilarityAn Explainable CNN Approach for Medical Codes Prediction from Clinical Text
Method: We develop CNN-based methods for automatic ICD coding based on clinical text from intensive care unit (ICU) stays. We come up with the Shallow and Wide Attention convolutional Mechanism (SWAM), which allows our m…
AttributeDocument ClassificationMedical Code PredictionMedical Codes PredictionOn-Device Document Classification using multimodal features
From small screenshots to large videos, documents take up a bulk of space in a modern smartphone. Documents in a phone can accumulate from various sources, and with the high storage capacity of mobiles, hundreds of docum…
ClassificationDocument ClassificationGeneral ClassificationModel Compression+2Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis
There is a significant demand for topic modeling on large-scale data with complex multi-modal structure in applications such as multi-layer network analysis, temporal document classification, and video data analysis; fre…
Document ClassificationBanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla
In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.…
Document ClassificationLanguage ModelingLanguage ModellingNatural Language Inference+5Automating Document Classification with Distant Supervision to Increase the Efficiency of Systematic Reviews
Objective: Systematic reviews of scholarly documents often provide complete and exhaustive summaries of literature relevant to a research question. However, well-done systematic reviews are expensive, time-demanding, and…
ArticlesDocument ClassificationGeneral ClassificationTAN-NTM: Topic Attention Networks for Neural Topic Modeling
Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of toke…
Document ClassificationKeyphrase GenerationTopic ModelsVariational InferenceJoint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora
In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual…
Bilingual Lexicon InductionClassificationCross-Lingual Word EmbeddingsDocument Classification+2Mixing ADAM and SGD: a Combined Optimization Method
Optimization methods (optimizers) get special attention for the efficient training of neural networks in the field of deep learning. In literature there are many papers that compare neural models trained with the use of …
Document ClassificationStochastic OptimizationvalidImproving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In most applications, users are not able t…
Conversational Question AnsweringDocument ClassificationQuestion AnsweringText Graph Transformer for Document Classification
Text classification is a fundamental problem in natural language processing. Recent studies applied graph neural network (GNN) techniques to capture global word co-occurrence in a corpus. However, previous works are not …
ClassificationDocument ClassificationGeneral ClassificationGraph Neural Network+3Neural Speed Reading Audited
Several approaches to neural speed reading have been presented at major NLP and machine learning conferences in 2017{--}20; i.e., {``}human-inspired{''} recurrent network architectures that learn to {``}read{''} text fas…
ClassificationDocument ClassificationMargin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent…
Cross-Lingual Document ClassificationDocument ClassificationDomain AdaptationNER+1Hierarchical Metadata-Aware Document Categorization under Weak Supervision
Categorizing documents into a given label hierarchy is intuitively appealing due to the ubiquity of hierarchical topic structures in massive text corpora. Although related studies have achieved satisfying performance in …
Data AugmentationDocument ClassificationRepresentation LearningRobust Document Representations using Latent Topics and Metadata
Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification problems. This technique is not adequate whe…
Document ClassificationLanguage ModelingLanguage ModellingGerman's Next Language Model
In this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, model size, and the presence of Whole Word …
BenchmarkingDocument ClassificationLanguage ModelingLanguage Modelling+5Exploiting Context for Robustness to Label Noise in Active Learning
Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available. Most of these works consider that labels obtained from…
Active LearningDocument ClassificationGeneral ClassificationScene ClassificationMulti-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi…
Document ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2