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Advanced Science, EarlyView.The ultrathin (2.5 nm) MXene film self‐assembled at liquid interfaces breaks the trade‐off between transparency (82.5 %) and photothermal conversion effect (DT∼25.1°C ± 2.9 °C) under 100 mW cm−2. After encapsulated by a slippery surface with low adhesion, this transparent film can be applied in smart windows, eyewear, and optical sensors to autonomous ...
Xiao Han +6 more
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Advanced Science, EarlyView.Schematic illustration of SiNDs composite materials synthesis and its internal photophysical process mechanism. And an AI‐assisted dynamic information encryption process. ABSTRACT Persistent luminescence materials typically encounter an intrinsic trade‐off between high phosphorescence quantum yield (PhQY) and ultralong phosphorescence lifetime.
Yulu Liu +9 more
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2021 Conference on Information Communications Technology and Society (ICTAS), 2021
Word embeddings are currently the most popular vector space model in Natural Language Processing. How we encode words is important because it affects the performance of many downstream tasks such as Machine Translation (MT), Information Retrieval (IR) and Automatic Speech Recognition (ASR).
Sibonelo Dlamini +3 more
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Word embeddings are currently the most popular vector space model in Natural Language Processing. How we encode words is important because it affects the performance of many downstream tasks such as Machine Translation (MT), Information Retrieval (IR) and Automatic Speech Recognition (ASR).
Sibonelo Dlamini +3 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both ...
Yang Liu +3 more
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Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both ...
Yang Liu +3 more
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Abstract This chapter deals with the mathematical representation of words through vectors or embeddings which are the basis of modern language models. It starts by discussing the limits of the one-hot representation and continues with a section that presents traditional approaches based on the factorization of the word co-occurrence ...
Christophe Gaillac, Jérémy L'Hour
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Christophe Gaillac, Jérémy L'Hour
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Sentiment Analysis with Word Embedding
2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST), 2018The basic task of sentiment analysis is to determine the sentiment polarity (positivity, neutrality or negativity) of a piece text. The traditional bag-of-words models deficiencies affect the accuracy of sentiment classifications. The purpose of this study is to improve the accuracy of the sentiment classification by employing the concept of word ...
B. Oscar Deho +3 more
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2022
Musical Word Embedding for Music Tagging and Retrieval IEEE Transactions on Audio, Speech and Language Processing (submitted) - SeungHeon Doh, Jongpil Lee, Dasaem Jeong, Juhan NamDEMO: https://seungheondoh.github.io/musical_word_embedding_demo/ Word embedding has become an essential means for text-based information retrieval.
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Musical Word Embedding for Music Tagging and Retrieval IEEE Transactions on Audio, Speech and Language Processing (submitted) - SeungHeon Doh, Jongpil Lee, Dasaem Jeong, Juhan NamDEMO: https://seungheondoh.github.io/musical_word_embedding_demo/ Word embedding has become an essential means for text-based information retrieval.
openaire +1 more source