Word Adjacency Graph Modeling

Regions Adjacency Graph Applied

Adjacency metric dimension of the 2-absorbing ideals graph

Let Γ=(V,E) be a graph and ‎W_(‎a)={w_1,…,w_k } be a subset of the vertices of Γ and v be a vertex of it. The k-vector r_2 (v∣ W_a)=(a_Γ (v,w_1),‎…‎ ,a_Γ (v,w_k)) is the adjacency representation of v with respect to W in which a_Γ (v,w_i )=min{2,d_Γ (v,w_i )} and d_Γ (v,w_i ) is the distance between v and w_i in Γ. W_a is called as an adjacency resolving set for Γ if distinct vertices of ...

Word duration modeling for word graph rescoring in LVCSR

A well-known unfavorable property of HMMs in speech recognition is their inappropriate representation of phone and word durations. This paper describes an approach to resolve this limitation by integrating explicit word duration models into an HMM-based speech recognizer. Word durations are represented by log-normal densities using a back-off strategy that approximates durations of words that h...

Lexical Coherence Graph Modeling Using Word Embeddings

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 o...

Graph Matching using Adjacency Matrix Markov Chains

This paper describes a spectral method for graph-matching. We adopt a graphical models viewpoint in which the graph adjacency matrix is taken to represent the transition probability matrix of a Markov chain. The nodeorder of the steady state random walk associated with this Markov chain is determined by the co-efficent order of the leading eigenvector of the adjacency matrix. We match nodes in ...