Search Results for author:
Found 10 papers, 4 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.
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels
Nowadays, the use of intelligent systems to detect redundant information in news articles has become especially prevalent with the proliferation of news media outlets in order to enhance user experience.
STA: Self-controlled Text Augmentation for Improving Text Classifications
Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult.
Topics as Entity Clusters: Entity-based Topics from Language Models and Graph Neural Networks
We demonstrate that our approach consistently outperforms other state-of-the-art topic models across coherency metrics and find that the explicit knowledge encoded in the graph-based embeddings provides more coherent topics than the implicit knowledge encoded with the contextualized embeddings of language models.
Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base
To evaluate the effectiveness of our approach, and due to the lack of datasets in this area, we also contribute to the research community with a gold-standard multilingual news-location dataset, NewsLOC.
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.
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.
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.
