Lexical Coherence Graph Modeling Using Word Embeddings

Abstract

Coherence is established by semantic connections between sentences of a text which can be modeled by lexical relations.In this paper, we introduce the lexical coherence graph (LCG), a new graph-based model to represent lexical relations among sentences.The frequency of subgraphs (coherence patterns) of this graph captures the connectivity style of sentence nodes in this graph.The coherence of a text is encoded by a vector of these frequencies.We evaluate the LCG model on the readability ranking task.The results of the experiments show that the LCG model obtains higher accuracy than state-of-the-art coherence models.Using larger subgraphs yields higher accuracy, because they capture more structural information.However, larger subgraphs can be sparse.We adapt Kneser-Ney smoothing to smooth subgraphs' frequencies.Smoothing improves performance.