Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving naturallanguage problems difficult. Recently, a number of text augmentation techniques have...
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Recent advancements in naturallanguageprocessing (NLP), notably the emergence of extensive language models pre-trained on vast datasets, are opening new avenues in Knowledge Engineering. This study delves into the u...
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The proceedings contain 22 papers. The topics discussed include: classifying organized criminal violence in Mexico using ML and LLMs;a multi-instance learning approach to civil unrest event detection on Twitter;BoschA...
ISBN:
(纸本)9789544520892
The proceedings contain 22 papers. The topics discussed include: classifying organized criminal violence in Mexico using ML and LLMs;a multi-instance learning approach to civil unrest event detection on Twitter;BoschAI @ Causal News Corpus 2023: robust cause-effect span extraction using multi-layer sequence tagging and data augmentation;an evaluation framework for mapping news headlines to event classes in a knowledge graph;Ometeotl@Multimodal hate speech event detection 2023: hate speech and text-image correlation detection in real life memes using pre-trained BERT models over text;InterosML@Causal News Corpus 2023: understanding causal relationships: supervised contrastive learning for event classification;ARC-NLP at multimodal hate speech event detection 2023: multimodal methods boosted by ensemble learning, syntactical and entity features;and lexical Squad@Multimodal hate speech event detection 2023: multimodal hate speech detection using fused ensemble approach.
A Knowledge graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse naturallanguageprocessing (NLP) tasks where knowledge is required. The need t...
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We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semiautomatic clustering of Word Usage graphs (WUGs). Unlike prior WSI datasets for Russ...
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language models encode linguistic proprieties and are used as input for more specific models. Using their word representations as-is for specialised and low-resource domains might be less efficient. methods of adaptin...
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As linguistic phenomena that showcase the richness and complexity of human language, puns pose significant challenges to naturallanguageprocessing (NLP) systems. The significance of these tasks lies not only in the ...
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Instruction-tuned large language models have revolutionized naturallanguageprocessing and have shown great potential in applications such as conversational agents. These models, such as GPT-4, can not only master la...
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naturallanguage Premise Selection (NLPS) is a mathematical naturallanguageprocessing (NLP) task that retrieves a set of useful relevant premises to support the end-user finding the proof for a particular statement....
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Developing imaging models capable of detecting pathologies from chest X-rays can be cost and time-prohibitive for large datasets as it requires supervision to attain state-of-the-art performance. Instead, labels extra...
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