This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs deep neural network to produce summary. A set of experiments were conducted evaluate performance and compare results those popular baseline word embedding called word2vec. new dataset was collected experiments. Two evaluation methodologies ...

We introduce a method for automatically labelling edges of word co-occurrence graphs with semantic relations. Therefore we only make use of training data already contained within the graph. Starting point of this work is a graph based on word co-occurrence of the German language, which is created by applying iterated co-occurrence analysis. The edges of the graph have been partially annotated b...

Chinese semantic dependency graph is extended from semantic dependency tree, which uses directed acyclic graphs to capture richer latent semantics of sentences. In this paper, we propose two approaches for Chinese semantic dependency graph parsing. In the first approach, we build a non-projective transition-based dependency parser with the Swap-based algorithm. Then we use a classifier to add a...

We present a method for summarizing document by creating a semantic graph of the original document and identifying the substructure of such a graph that can be used to extract sentences for a document summary. We start with deep syntactic analysis of the text and, for each sentence, extract logical form triples, subject–predicate–object. We then apply cross-sentence pronoun resolution, co-refer...

This paper explores basic level of semantic structure formation in the human vision inferential processes in line with Gestalt laws and proposes micro level semantic structure formations and their relational combinations. Using this approach two sets of semantic features have been derived for visual object class recognition. The first algorithm uses the hypothesis in line with Gestalt laws of p...

This paper proposes a novel latent semantic learning method for extracting high-level latent semantics from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of mid-level features, we develop a graph-based...