Attention in natural language processing
Attention is an increasingly popular mechanism used in a wide range of neural
architectures. The mechanism itself has been realized in a variety of formats. However …
architectures. The mechanism itself has been realized in a variety of formats. However …
Opportunities and challenges of ChatGPT for design knowledge management
Abstract Recent advancements in Natural Language Processing have opened up new
possibilities for the development of large language models like ChatGPT, which can …
possibilities for the development of large language models like ChatGPT, which can …
A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Improving multi-hop question answering over knowledge graphs using knowledge base embeddings
Abstract Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …
Rotate: Knowledge graph embedding by relational rotation in complex space
We study the problem of learning representations of entities and relations in knowledge
graphs for predicting missing links. The success of such a task heavily relies on the ability of …
graphs for predicting missing links. The success of such a task heavily relies on the ability of …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Knowledge graph embedding based question answering
Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge
graph (KG) to answer natural language questions. It helps end users more efficiently and …
graph (KG) to answer natural language questions. It helps end users more efficiently and …
Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences
Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …
recommendation accuracy and explainability. However, existing methods largely assume …
A novel embedding model for knowledge base completion based on convolutional neural network
In this paper, we propose a novel embedding model, named ConvKB, for knowledge base
completion. Our model ConvKB advances state-of-the-art models by employing a …
completion. Our model ConvKB advances state-of-the-art models by employing a …
Leveraging meta-path based context for top-n recommendation with a neural co-attention model
Heterogeneous information network (HIN) has been widely adopted in recommender
systems due to its excellence in modeling complex context information. Although existing …
systems due to its excellence in modeling complex context information. Although existing …