Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian
Addressing the challenge of limited annotated data in specialized fields and low-resource languages is crucial for the effective use of Language Models (LMs). While most Large Language Models (LLMs) are trained on genera…
Document ClassificationEntity TypingGeneral Knowledgezero-shot-classification+1An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry
The task of $\textit{keyword extraction}$ is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document classification. While recent methods hav…
Document ClassificationKeyword ExtractionHierarchical Multi-modal Transformer for Cross-modal Long Document Classification
Long Document Classification (LDC) has gained significant attention recently. However, multi-modal data in long documents such as texts and images are not being effectively utilized. Prior studies in this area have attem…
Document ClassificationSentenceRapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine
This paper introduces the Pandemic PACT Advanced Categorisation Engine (PPACE) along with its associated dataset. PPACE is a fine-tuned model developed to automatically classify research abstracts from funded biomedical …
Decision MakingDocument ClassificationLanguage ModellingLarge Language ModelSuperGLEBer: German Language Understanding Evaluation Benchmark
We assemble a broad Natural Language Understanding benchmark suite for the German language and consequently evaluate a wide array of existing German-capable models in order to create a better understanding of the current…
Document ClassificationNatural Language UnderstandingQuestion AnsweringSentence+1DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these documents and assessing large models' abiliti…
Document ClassificationVisual GroundingFocus on the Core: Efficient Attention via Pruned Token Compression for Document Classification
Transformer-based models have achieved dominant performance in numerous NLP tasks. Despite their remarkable successes, pre-trained transformers such as BERT suffer from a computationally expensive self-attention mechanis…
Document ClassificationAuxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification
The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing assigns a subset of ICD codes t…
Document ClassificationEvaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark
Background The ability of large language models (LLMs) to interpret and generate human-like text has been accompanied with speculation about their application in medicine and clinical research. There is limited data avai…
Document ClassificationLanguage ModelingLanguage ModellingLarge Language Model+8Length-Aware Multi-Kernel Transformer for Long Document Classification
Lengthy documents pose a unique challenge to neural language models due to substantial memory consumption. While existing state-of-the-art (SOTA) models segment long texts into equal-length snippets (e.g., 128 tokens per…
Document ClassificationSentenceImproving Long Text Understanding with Knowledge Distilled from Summarization Model
Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as n…
Abstractive Text SummarizationDocument ClassificationOpen-Domain Question AnsweringQuestion Answering+2CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification
Zero-shot learning has been extensively investigated in the broader field of visual recognition, attracting significant interest recently. However, the current work on zero-shot learning in document image classification …
Document Classificationdocument-image-classificationDocument Image ClassificationGeneralized Zero-Shot Learning+3Machine Unlearning for Document Classification
Document understanding models have recently demonstrated remarkable performance by leveraging extensive collections of user documents. However, since documents often contain large amounts of personal data, their usage ca…
ClassificationDocument Classificationdocument understandingMachine UnlearningL3Cube-MahaNews: News-based Short Text and Long Document Classification Datasets in Marathi
The availability of text or topic classification datasets in the low-resource Marathi language is limited, typically consisting of fewer than 4 target labels, with some achieving nearly perfect accuracy. In this work, we…
ArticlesDocument Classificationtext-classificationText Classification+1GuideWalk: A Novel Graph-Based Word Embedding for Enhanced Text Classification
One of the prime problems of computer science and machine learning is to extract information efficiently from large-scale, heterogeneous data. Text data, with its syntax, semantics, and even hidden information content, p…
ClassificationDocument Classificationtext-classificationText ClassificationBuDDIE: A Business Document Dataset for Multi-task Information Extraction
The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in a multi-modal domain. Several datasets exist for research on specific tasks of VRDU such as document clas…
Document Classificationdocument understandingEntity LinkingLanguage Modelling+4Developing Healthcare Language Model Embedding Spaces
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. We explore specialized pre-training to adapt smaller LLMs to different healthcare datasets. Three methods ar…
Contrastive LearningDocument ClassificationLanguage ModelingLanguage Modelling+2Visually Guided Generative Text-Layout Pre-training for Document Intelligence
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and reason both document texts and layouts (e…
Document Classificationdocument understandingOptical Character Recognition (OCR)Question AnsweringNextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents
While (large) language models have significantly improved over the last years, they still struggle to sensibly process long sequences found, e.g., in books, due to the quadratic scaling of the underlying attention mechan…
Document ClassificationLanguage ModelingLanguage ModellingMasked Language Modeling+3Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documents
We propose a novel clustering pipeline to detect and characterize influence campaigns from documents. This approach clusters parts of document, detects clusters that likely reflect an influence campaign, and then identif…
ClusteringDocument Classification