Knowledge-driven graph similarity for text classification

Abstract

Automatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through the weighting and enrichment of a graph-based representation. We introduce weighted co-occurrence graphs to represent text documents, which weight the terms and their dependencies based on their relevance to text classification. We propose a novel method to automatically enrich the weighted graphs using semantic knowledge in the form of a word similarity matrix. The similarity between enriched graphs, knowledge-driven graph similarity, is calculated using a graph kernel. The semantic knowledge in the enriched graphs ensures that the graph kernel goes beyond exact matching of terms and patterns to compute the semantic similarity of documents. In the experiments on sentiment classification and topic classification tasks, our knowledge-driven similarity measure significantly outperforms the baseline text similarity measures on five benchmark text classification datasets.

Domain-agnostic document and question classification using natural language processing techniques

“…On the other hand, phrases such as the right, the room, this picture, and this mini lab, which refer to a particular situation, belong to the Context sensitive class label. This observation is in line with the importance of semantic similarity in knowledge-driven graphs for text classification applications beyond domain-agnostic class labels such as sentiment analysis that is based on sentence polarity [184]. Nevertheless, it is worth noting that all four graphs are required for good classification performance due to the advantages of each dimension.…”

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

“…According to the literature, a lot of work is done on semantic analysis in biomedical text, which offers great potential for identifying semantic relationships between biomedical entities, terms, and terminologies [52,53]. Semantic analysis in the biomedical domain employs multiple NLP tasks, including WSD [54], clustering [55], ontology learning [56], information retrieval [33], text classification [57], question answering [39,41,42], text Summarization [58], topic detection [58], and many others. Extracting semantic similarity implies determining and quantifying the contextual relationship between concepts based on shared features.…”

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

“…The first one aims to find a motif between possibly two different-sized graphs (in terms of nodes and edges) to deduct a result, e.g., a deduction that both graphs have similar network dynamics. Here, a recent work in this area is worth mentioning 12 . It employed graph theory techniques for text classification.…”

“…It employed graph theory techniques for text classification. In 12 , NLP researchers first convert sentences into adjacency matrices based on the co-occurrence of words, and then to graphs. The process then efficiently predicts word similarities by employing graph similarity techniques.…”