Titlebook: Natural Language Processing and Information Systems; 26th International C Elisabeth Métais,Farid Meziane,Epaminondas Kapetan Conference pro

過濾

Cross-Domain Transfer of Generative Explanations Using Text-to-Text Modelsce across various natural language processing tasks. However, these models remain opaque and hard to explain due to their vast complexity and size. This limits adoption in highly-regulated domains like medicine and finance, and often there is a lack of trust from non-expert end-users. In this paper,

杠桿

Virus Causes Flu: Identifying Causality in the Biomedical Domain Using an Ensemble Approach with Taromain. An example sentence having CE relation in the biomedical domain (precisely Leukemia) is: .. Here, . is the cause argument, . is the effect argument and . is the trigger word creating a causal scenario. Notably CE relation has a temporal order between . and . arguments. In this paper, we harne

ADOPT

Gerontology

colloquial

Sequence-Based Word Embeddings for?Effective Text Classification generalize LME to consider different distance metrics and address existing scalability issues using negative sampling, thus making DiVe scalable for large datasets. In order to evaluate the quality of word embeddings produced by DiVe, we used them to train standard machine learning classifiers, wit

Vo2-Max

書法

Multiword Expression Features for?Automatic Hate Speech Detection human monitoring of hate speech is unfeasible. In this work, we propose new word-level features for automatic hate speech detection (HSD): multiword expressions (MWEs). MWEs are lexical units greater than a word that have idiomatic and compositional meanings. We propose to integrate MWE features in