Search Results for author:
Found 9 papers, 3 papers with code
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization
Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.
Real-Time Keyword Extraction from Conversations
We introduce a novel method to extract keywords from meeting speech in real-time.
Graph Classification with 2D Convolutional Neural Networks
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.
Ranked #3 on
Graph Classification
on RE-M12K
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
Shortest-Path Graph Kernels for Document Similarity
In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents.
Kernel Graph Convolutional Neural Networks
Graph kernels have been successfully applied to many graph classification problems.
Kernel Graph Convolutional Neural Nets
Graph kernels have been successfully applied to many graph classification problems.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
Ranked #1 on
Meeting Summarization
on ICSI Meeting Corpus
Abstractive Dialogue Summarization
Abstractive Text Summarization
+6
Word Embeddings from Large-Scale Greek Web Content
Word embeddings are undoubtedly very useful components in many NLP tasks.
