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Cannabinoid CB1 Receptor in Nociceptors Mediates Postoperative Analgesia via ASIC3 Inhibition
Advanced Science, EarlyView.CB1R and ASIC3 are co‐expressed in CGRP+ nociceptors of mouse DRG. Plantar incision triggers an upregulation of ASIC3 and activation of ASIC3+ neurons, driving mechanical allodynia. CB1R activation suppresses ASIC3 function and downstream ERK1/2 phosphorylation, thereby braking nociceptive signaling and accelerating pain resolution.
Kangtai Xu +13 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 0005 +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 0005 +3 more
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Word Embedding Evaluation and Combination
Proceedings of the Language Resources and Evaluation Conference, 2016no ...
Ghannay, Sahar +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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Chinese Word Embeddings with Subwords
Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018Word embeddings are very useful in variety of natural language processing tasks. Recently, more researches have focused on learning word embeddings with morphological knowledge of words, such as character and subword information. In this paper, we present a new method to use subwords and characters together to enhance word embeddings (SWE).
Gang Yang, Hongzhe Xu, Wen Li
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