Papers Multilabel Text Classification
“Multilabel Text Classification” 태그가 달린 논문 12편 · 필터 해제
ChuLo: Chunk-Level Key Information Representation for Long Document Processing
Transformer-based models have achieved remarkable success in various Natural Language Processing (NLP) tasks, yet their ability to handle long documents is constrained by computational limitations. Traditional approaches…
ChunkingClassificationDocument Classificationdocument understanding+5A Novel ICD Coding Method Based on Associated and Hierarchical Code Description Distillation
ICD(International Classification of Diseases) coding involves assigning ICD codes to patients visit based on their medical notes. ICD coding is a challenging multilabel text classification problem due to noisy medical do…
Multilabel Text ClassificationRepresentation Learningtext-classificationText ClassificationA Two-Stage Decoder for Efficient ICD Coding
Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures. ICD coding is a challenging multilabel…
ClassificationDecoderMultilabel Text Classificationtext-classification+1What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text Classification With Human-in-the-Loop Curation for Automatic Labeling of Patient Self Reports of Problems
The USA Food and Drug Administration has accorded increasing importance to patient-reported problems in clinical and research settings. In this paper, we explore one of the largest online datasets comprising 170,141 open…
Multilabel Text ClassificationMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response
Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annota…
HumanitarianMultilabel Text ClassificationMultilingual NLPtext annotationAdversarial Examples for Extreme Multilabel Text Classification
Extreme Multilabel Text Classification (XMTC) is a text classification problem in which, (i) the output space is extremely large, (ii) each data point may have multiple positive labels, and (iii) the data follows a stron…
ClassificationMultilabel Text ClassificationRecommendation Systemstext-classification+1Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training
Virtual Adversarial Training (VAT) has been effective in learning robust models under supervised and semi-supervised settings for both computer vision and NLP tasks. However, the efficacy of VAT for multilingual and mult…
Emotion RecognitionMultilabel Text Classificationtext-classificationText ClassificationGeneralized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification
\emph{Funnelling} (Fun) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classi…
Ensemble LearningMultilabel Text Classificationtext-classificationText Classification+1Layer-wise Guided Training for BERT: Learning Incrementally Refined Document Representations
Although BERT is widely used by the NLP community, little is known about its inner workings. Several attempts have been made to shed light on certain aspects of BERT, often with contradicting conclusions. A much raised c…
General ClassificationMultilabel Text Classificationtext-classificationText ClassificationEstablishing Baselines for Text Classification in Low-Resource Languages
While transformer-based finetuning techniques have proven effective in tasks that involve low-resource, low-data environments, a lack of properly established baselines and benchmark datasets make it hard to compare diffe…
ClassificationGeneral ClassificationMultilabel Text Classificationtext-classification+1CAWA: An Attention-Network for Credit Attribution
Credit attribution is the task of associating individual parts in a document with their most appropriate class labels. It is an important task with applications to information retrieval and text summarization. When label…
Information RetrievalMultilabel Text ClassificationRetrievalSentence+3Text segmentation on multilabel documents: A distant-supervised approach
Segmenting text into semantically coherent segments is an important task with applications in information retrieval and text summarization. Developing accurate topical segmentation requires the availability of training d…
Information RetrievalMultilabel Text ClassificationRetrievalSegmentation+4