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Found 11 papers, 2 papers with code
Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models
Furthermore, in order to make more meaningful comparisons with theories of human language comprehension in psycholinguistics, we focus on two key stages where the meaning of a particular target word may arise: immediately before the word’s presentation to the model (comparable to forward inferencing), and immediately after the word token has been input into the network.
Deep Learning Proteins using a Triplet-BERT network
Modern sequencing technology has produced a vast quantity of proteomic data, which has been key to the development of various deep learning models within the field.
Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network
In this paper, we seek to leverage a set of BERT-style models that have been pre-trained on vast quantities of both protein and drug data.
Encoding Lexico-Semantic Knowledge using Ensembles of Feature Maps from Deep Convolutional Neural Networks
Semantic models derived from visual information have helped to overcome some of the limitations of solely text-based distributional semantic models.
Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models
Researchers have recently demonstrated that tying the neural weights between the input look-up table and the output classification layer can improve training and lower perplexity on sequence learning tasks such as language modelling.
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment
Previous work in the area of sentiment analysis has focused on using information from within a sentence to predict a valence value for that sentence.
Feature2Vec: Distributional semantic modelling of human property knowledge
We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features.
Analysing Representations of Memory Impairment in a Clinical Notes Classification Model
Despite recent advances in the application of deep neural networks to various kinds of medical data, extracting information from unstructured textual sources remains a challenging task.
Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model
Performance in language modelling has been significantly improved by training recurrent neural networks on large corpora.
Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge
In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding.
