Search Results for author:
Found 17 papers, 5 papers with code
Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data
Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive embeddings for various applications such as retrieval, question answering, and classification.
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature.
Context-Aware Clustering using Large Language Models
Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and effective supervised clustering of entity subsets, particularly focusing on text-based entities.
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce
This lack of interpretability hinders the development and adoption of new techniques in the field.
Complex Logical Reasoning over Knowledge Graphs using Large Language Models
Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations.
Text Enriched Sparse Hyperbolic Graph Convolutional Networks
In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer.
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
Such an approach enables us to propose a hyperbolic normalization layer and to further simplify the entire hyperbolic model to a Euclidean model cascaded with our hyperbolic normalization layer.
Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximations
However, their adoption in practice remains restricted due to (i) non-scalability on accelerated deep learning hardware, (ii) vanishing gradients due to the closure of hyperbolic space, and (iii) information loss due to frequent mapping between local tangent space and fully hyperbolic space.
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection.
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Promising approaches to tackle this problem include embedding the KG units (e. g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results.
Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction
In this paper, we leverage social media platforms such as twitter for developing corpus across multiple languages.
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages
The model learns the representation of resource-poor and resource-rich sentences in a common space by using the similarity between their assigned annotation tags.
Automatic Normalization of Word Variations in Code-Mixed Social Media Text
Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies.
Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks
Machine learning approaches in sentiment analysis principally rely on the abundance of resources.
Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages
Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages.
Contrastive Learning of Emoji-based Representations for Resource-Poor Languages
The model learns the representations of resource-poor and resource-rich language in a common emoji space by using a similarity metric based on the emojis present in sentences from both languages.
Neural Network Architecture for Credibility Assessment of Textual Claims
CREDO consists of different modules for capturing various features responsible for the credibility of an article.
