Papers Topic Classification
“Topic Classification” 태그가 달린 논문 186편 · 필터 해제
Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs
The analysis of public affairs documents is crucial for citizens as it promotes transparency, accountability, and informed decision-making. It allows citizens to understand government policies, participate in public disc…
Decision MakingTopic ClassificationEfficient Document Embeddings via Self-Contrastive Bregman Divergence Learning
Learning quality document embeddings is a fundamental problem in natural language processing (NLP), information retrieval (IR), recommendation systems, and search engines. Despite recent advances in the development of tr…
Contrastive LearningInformation RetrievalRecommendation SystemsRetrieval+3Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain
A common way to use large pre-trained language models for downstream tasks is to fine tune them using additional layers. This may not work well if downstream domain is a specialized domain whereas the large language mode…
Language ModelingLanguage ModellingLarge Language Modeltext-classification+2Zero-Shot Text Classification via Self-Supervised Tuning
Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scale annotated data of relevant tasks for m…
ClassificationSelf-Supervised LearningSentenceSentiment Analysis+4A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot
Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices…
Emotion ClassificationQuestion AnsweringTopic ClassificationVideo UnderstandingQuantifying the Dissimilarity of Texts
Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, …
ClusteringInformation RetrievalRetrievalTopic ClassificationMasakhaNEWS: News Topic Classification for African languages
African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only …
ClassificationFew-Shot LearningMachine Translationnamed-entity-recognition+5Deep Learning for Opinion Mining and Topic Classification of Course Reviews
Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volu…
Opinion MiningTopic ClassificationWord EmbeddingsWhen Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona Dialogue Corpus
Building a natural language dataset requires caution since word semantics is vulnerable to subtle text change or the definition of the annotated concept. Such a tendency can be seen in generative tasks like question-answ…
Dialogue GenerationQuestion AnsweringSentiment AnalysisTopic ClassificationTopic Segmentation Model Focusing on Local Context
Topic segmentation is important in understanding scientific documents since it can not only provide better readability but also facilitate downstream tasks such as information retrieval and question answering by creating…
Information RetrievalmodelMulti-Task LearningQuestion Answering+6QBERT: Generalist Model for Processing Questions
Using a single model across various tasks is beneficial for training and applying deep neural sequence models. We address the problem of developing generalist representations of text that can be used to perform a range o…
modelQuestion AnsweringTopic ClassificationTCBERT: A Technical Report for Chinese Topic Classification BERT
Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance. Variants customized for different lang…
ClassificationContrastive LearningTopic ClassificationTEMPERA: Test-Time Prompting via Reinforcement Learning
Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning. As a consequence, there is a growing interest in automated methods to design optimal prompts. In this work, we prop…
Few-Shot LearningNatural Language InferenceReading Comprehensionreinforcement-learning+4CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task-specific indicators and language verbal…
ClassificationcounterfactualEntity TypingMachine Translation+6Hierarchical Multi-Label Classification of Scientific Documents
Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection. With the large number of topics being available in recent years, it has become nec…
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence prediction (NSP) employ cross-encoder archi…
ClassificationNatural Language InferenceSentencetext-classification+3Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more acces…
Abstractive Text SummarizationFeature ImportanceSentiment Analysistext-classification+2HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea
Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters and not understood by modern Korean or Chinese speakers. Historians with expertise in t…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Retrieval+1Twitter Topic Classification
Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is re…
ClassificationTopic ClassificationNon-Parametric Temporal Adaptation for Social Media Topic Classification
User-generated social media data is constantly changing as new trends influence online discussion and personal information is deleted due to privacy concerns. However, most current NLP models are static and rely on fixed…
ClassificationRetrievalTopic Classification