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pro vyhledávání: '"Papay, Sean"'
Linear-chain conditional random fields (CRFs) are a common model component for sequence labeling tasks when modeling the interactions between different labels is important. However, the Markov assumption limits linear-chain CRFs to only directly mode
Externí odkaz:
http://arxiv.org/abs/2411.12484
Autor:
Schäfer, Johannes, Combs, Aidan, Bagdon, Christopher, Li, Jiahui, Probol, Nadine, Greschner, Lynn, Papay, Sean, Resendiz, Yarik Menchaca, Velutharambath, Aswathy, Wührl, Amelie, Weber, Sabine, Klinger, Roman
Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message addressed to a "bro", but a male teenager might find it appropriate. It
Externí odkaz:
http://arxiv.org/abs/2410.08820
The identification of political actors who put forward claims in public debate is a crucial step in the construction of discourse networks, which are helpful to analyze societal debates. Actor identification is, however, rather challenging: Often, th
Externí odkaz:
http://arxiv.org/abs/2402.00620
A major challenge in structured prediction is to represent the interdependencies within output structures. When outputs are structured as sequences, linear-chain conditional random fields (CRFs) are a widely used model class which can learn \textit{l
Externí odkaz:
http://arxiv.org/abs/2106.07306
Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text. Despite being a staple of NLP, and sharing a common structure, there is little insight on
Externí odkaz:
http://arxiv.org/abs/2010.02587
We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling. Our system focuses on Vector Initialization (VI) alignment, compares VI to t
Externí odkaz:
http://arxiv.org/abs/2008.03164
Autor:
Chen, Jun, Papay, Sean
We propose that the Mandarin sentence-final particle de is attached to answers with maximal utility. We quantify this utility as informativity by drawing on a cross-entropy model, and explore the plausibility of applying cross-entropy methods (as wel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d23b230ff5f63b5b9f7e3d34b445f1b7