Results 71 to 80 of about 1,006,138 (295)
Word Embeddings through Hellinger PCA [PDF]
, 2014 Word embeddings resulting from neural lan- guage models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming.
Ronan Collobert +3 more
core +3 more sources
Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation
FEBS Letters, EarlyView.Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang +2 more
wiley +1 more source
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy [PDF]
, 2021 Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well its context.
Bollegala, D, Zhou, Y
core +1 more source
Compressing Word Embeddings [PDF]
, 2016 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental results were submitted to ICLR-2016, but the paper has been significantly updated, since a new experimental set-up worked much ...
openaire +4 more sources
Liver organoids: modelling complexity in homeostasis and disease
FEBS Letters, EarlyView.Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley +1 more source
Spoken-Word Recognition: The Access to Embedded Words
Brain and Language, 1999 Two cross-modal priming experiments investigated whether the representation of either an initial- or a final-embedded word may be activated when the longer carrier word is auditorily presented. Visual targets were semantically related either to the embedded word or to the carrier word or they were unrelated to the primes. A priming effect was found for
Isel, F., Bacri, N.
openaire +3 more sources
A Joint Model for Word Embedding and Word Morphology [PDF]
Proceedings of the 1st Workshop on Representation Learning for NLP, 2016 This paper presents a joint model for performing unsupervised morphological analysis on words, and learning a character-level composition function from morphemes to word embeddings. Our model splits individual words into segments, and weights each segment according to its ability to predict context words.
Rei, Marek, Cao, Kris
openaire +4 more sources
Semantic Author Name Disambiguation with Word Embeddings [PDF]
, 2017 We present a supervised machine learning AND system which tackles semantic similarity between publication titles by means of word embeddings. Word embeddings are integrated as external components, which keeps the model small and efficient, while allowing
Müller, Mark-Christoph +1 more
core +1 more source
FEBS Letters, EarlyView.Vacuolar H+‐ATPase (V‐ATPase) is a multisubunit rotary proton pump that acidifies intracellular compartments. Using elastic network models, we identified conserved hinge residues that govern its large‐scale functional motions. We show that intrinsically encoded ‘soft modes’ drive critical functional transitions, including rotary catalysis and ...
Diksha Parwana +3 more
wiley +1 more source
Lexicon-Enhanced LSTM With Attention for General Sentiment Analysis
IEEE Access, 2018 Long short-term memory networks (LSTMs) have gained good performance in sentiment analysis tasks. The general method is to use LSTMs to combine word embeddings for text representation.
Xianghua Fu +4 more
doaj +1 more source