Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Unsupervised Document Representation using Partition Word-Vectors Averaging
Learning effective document-level representation is essential in many important NLP tasks such as document classification, summarization, etc. Recent research has shown that simple weighted averaging of word vectors is a…
Document ClassificationSentenceAn Analysis of Hierarchical Text Classification Using Word Embeddings
Efficient distributed numerical word representation models (word embeddings) combined with modern machine learning algorithms have recently yielded considerable improvement on automatic document classification tasks. How…
BIG-bench Machine LearningClassificationDocument ClassificationGeneral Classification+3Gaussian Word Embedding with a Wasserstein Distance Loss
Compared with word embedding based on point representation, distribution-based word embedding shows more flexibility in expressing uncertainty and therefore embeds richer semantic information when representing words. The…
Document ClassificationGeneral ClassificationWord SimilarityLinked Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine transla…
Document ClassificationMachine Translationspeech-recognitionSpeech Recognition+1Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over v…
DiversityDocument ClassificationMultilingual text classificationMultiview Learning+1Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced…
Document ClassificationGeneral ClassificationMachine TranslationParaphrase Identification+7Large-Scale Learnable Graph Convolutional Networks
Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters en…
Document ClassificationInductive LearningNode ClassificationPaying Attention to Attention: Highlighting Influential Samples in Sequential Analysis
In (Yang et al. 2016), a hierarchical attention network (HAN) is created for document classification. The attention layer can be used to visualize text influential in classifying the document, thereby explaining the mode…
Document ClassificationT\"ubingen-Oslo Team at the VarDial 2018 Evaluation Campaign: An Analysis of N-gram Features in Language Variety Identification
This paper describes our systems for the VarDial 2018 evaluation campaign. We participated in all language identification tasks, namely, Arabic dialect identification (ADI), German dialect identification (GDI), discrimin…
Dialect IdentificationDocument ClassificationLanguage IdentificationUsing J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models
K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires random partitioning…
Document ClassificationGeneral ClassificationModel SelectionPart-Of-Speech Tagging+2Authorless Topic Models: Biasing Models Away from Known Structure
Most previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the exact opposite is often true. Som…
Document ClassificationTopic ModelsWord EmbeddingsNMT-based Cross-lingual Document Embeddings
This paper investigates a cross-lingual document embedding method that improves the current Neural machine Translation framework based Document Vector (NTDV or simply NV). NV is developed with a self-attention mechanism …
Cross-Lingual Document ClassificationDocument ClassificationDocument EmbeddingGeneral Classification+3Multi-view Reconstructive Preserving Embedding for Dimension Reduction
With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can re ect various perspectives of one same sample…
Dimensionality ReductionDocument ClassificationFace RecognitionImage Retrieval+1Batch IS NOT Heavy: Learning Word Representations From All Samples
Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations. However, it is known that sampling methods are biased especially when the sampling distribution devia…
AllDocument ClassificationNamed Entity Recognition (NER)Word EmbeddingsSubword-level Word Vector Representations for Korean
Research on distributed word representations is focused on widely-used languages such as English. Although the same methods can be used for other languages, language-specific knowledge can enhance the accuracy and richne…
Document ClassificationLanguage ModelingLanguage ModellingMachine Translation+3A Multi-task Approach to Learning Multilingual Representations
We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together wit…
Cross-Lingual Document ClassificationDocument ClassificationGeneral ClassificationSentence+3Predicting accuracy on large datasets from smaller pilot data
Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from model…
Document ClassificationExamining Temporality in Document Classification
Many corpora span broad periods of time. Language processing models trained during one time period may not work well in future time periods, and the best model may depend on specific times of year (e.g., people might des…
ClassificationDocument ClassificationDomain AdaptationGeneral ClassificationHarriGT: A Tool for Linking News to Science
Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society. Linking these articles is …
ArticlesDocument ClassificationEntity Extraction using GANInformation Retrieval+1Multi-glance Reading Model for Text Understanding
In recent years, a variety of recurrent neural networks have been proposed, e.g LSTM. However, existing models only read the text once, it cannot describe the situation of repeated reading in reading comprehension. In fa…
Document ClassificationMachine TranslationmodelReading Comprehension+1