Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting
In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real-world events, e.…
Document ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRevisiting Transformer-based Models for Long Document Classification
The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficie…
ClassificationDocument Classificationtext-classificationText ClassificationTemporal Language Modeling for Short Text Document Classification with Transformers
Language models are typically trained on solely text data, not utilizing documents timestamp, which is available in most internet corpora. In this paper, we examine the impact of incorporating timestamp into transformer …
ClassificationDocument ClassificationLanguage ModelingLanguage Modelling+1MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity
Similarity is a comparative-subjective measure that varies with the domain within which it is considered. In several NLP applications such as document classification, pattern recognition, chatbot question-answering, sent…
ChatbotDocument ClassificationQuestion AnsweringSemantic Similarity+4Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure. Contrastive learning (CL) is an increasingly popular paradigm for such settings a…
ClassificationContrastive LearningData AugmentationDocument Classification+5Feature Selective Likelihood Ratio Estimator for Low- and Zero-frequency N-grams
In natural language processing (NLP), the likelihood ratios (LRs) of N-grams are often estimated from the frequency information. However, a corpus contains only a fraction of the possible N-grams, and most of them occur …
Document Classificationfeature selectionComparative Study of Long Document Classification
The amount of information stored in the form of documents on the internet has been increasing rapidly. Thus it has become a necessity to organize and maintain these documents in an optimum manner. Text classification alg…
BIG-bench Machine LearningClassificationDocument Classificationtext-classification+1Domain-adaptation of spherical embeddings
Domain adaptation of embedding models, updating a generic embedding to the language of a specific domain, is a proven technique for domains that have insufficient data to train an effective model from scratch. Chemistry …
Document ClassificationDomain AdaptationLanguage ModelingLanguage ModellingLegal Terminology Extraction with the Termolator
Domain-specific terminology is ubiquitous in legal documents. Despite potential utility in populating glossaries and ontologies or as arguments in information extraction and document classification tasks, there has been …
ArticlesDocument ClassificationEffectively Leveraging BERT for Legal Document Classification
Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art performances on several text classification tasks, such as GLUE and sentiment analysis. Recent work in the legal domain started…
Binary ClassificationClassificationDocument ClassificationSentiment Analysis+2Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs
Multi-label document classification, associating one document instance with a set of relevant labels, is attracting more and more research attention. Existing methods explore the incorporation of information beyond text,…
ClassificationDocument ClassificationEffective Convolutional Attention Network for Multi-label Clinical Document Classification
Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional …
ClassificationDocument ClassificationMedical Code PredictionMultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels…
Cross-Lingual TransferDocument ClassificationTopic ClassificationZero-Shot Cross-Lingual TransferSoftmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large
Classification problems having thousands or more classes naturally occur in NLP, for example language models or document classification. A softmax or one-vs-all classifier naturally handles many classes, but it is very s…
Document ClassificationDomain Agnostic Few-Shot Learning For Document Intelligence
Few-shot learning aims to generalize to novel classes with only a few samples with class labels. Research in few-shot learning has borrowed techniques from transfer learning, metric learning, meta-learning, and Bayesian …
ClassificationCross-Domain Few-Shotcross-domain few-shot learningDocument Classification+8Contrastive Document Representation Learning with Graph Attention Networks
Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention complexity, most of the pretrained Trans…
Contrastive LearningDocument ClassificationGraph AttentionRepresentation Learning+1Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization
Long text understanding is important yet challenging in natural language processing. A long article or essay usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise…
Abstractive Text SummarizationDocument ClassificationOpen-Domain Question AnsweringQuestion Answering+2Weakly Supervised Concept Map Generation through Task-Guided Graph Translation
Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts. Traditional unsupervised methods do …
Document ClassificationTranslationJOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING
Document embedding generally ignores underlying topics, which fails to capture polysemous terms that can mislead to improper thematic representation. Moreover, embedding a new document during the test process needs a com…
Document ClassificationDocument EmbeddingWord EmbeddingsTowards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification
Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty --- \emph{35 U.S. Code § 102} rejects more recent applications tha…
ClassificationDocument Classification