Graph-based Natural Language Processing and Information by Rada F. Mihalcea, Dragomir R. Radev
By Rada F. Mihalcea, Dragomir R. Radev
Graph idea and the fields of usual language processing and data retrieval are well-studied disciplines. generally, those parts were perceived as distinctive, with diverse algorithms, diverse functions, and various strength end-users. in spite of the fact that, contemporary examine has proven that those disciplines are in detail attached, with a wide number of average language processing and knowledge retrieval purposes discovering effective recommendations inside graph-theoretical frameworks. This e-book widely covers using graph-based algorithms for normal language processing and data retrieval. It brings jointly subject matters as diversified as lexical semantics, textual content summarization, textual content mining, ontology building, textual content type, and knowledge retrieval, that are hooked up via the typical underlying subject matter of using graph-theoretical tools for textual content and data processing projects. Readers will come away with a company realizing of the main tools and purposes in typical language processing and data retrieval that depend on graph-based representations and algorithms.
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Additional resources for Graph-based Natural Language Processing and Information Retrieval
Another error-tolerant graph-matching algorithm is the graph edit distance, which measures the dissimilarity of two graphs G and H as the number of changes needed to transform one graph into the other. Thus, the edit distance is determined as the number of edge additions and edge deletions required to transform G into H . 12. A bipartite graph showing which phrases appear in each document in a small document collection. 7. Dimensionality Reduction DIMENSIONALITY REDUCTION LATENT SEMANTIC ANALYSIS An important technique in data analysis with wide applications to information retrieval and natural language processing is dimensionality reduction.
That proves f (x) = g(x) for all x. 3. Reaching the Boundary Theorem: Given h(x) defined on sequence S, a random walker will reach either 0 or N in the sequence S. 18. A sheet of metal used to illustrate the heat equation. Proof: Let h(x) = 1/2h(x − 1) + 1/2h(x + 1), so that h(x) is harmonic. Therefore, h(0) = h(N ) = 0. According to the maximum principle, h(x) = 0 for all x. A harmonic function can be interpreted in a random walk framework. The value of the harmonic function at a given node i is equal to the probability of the random walk starting at that node and reaching a positive-labeled node.
In the case of unweighted graphs, the length of a path is calculated as the number of edges on the path. 7. Shortest paths starting with the source node C. graphs, the length is calculated as the sum of the weights of all edges on the path. The algorithm used for the case of weighted graphs also is referred to as Dijkstra’s algorithm. 7(a) and assume that we start with the source node C. The length of the shortest path from C to C is obviously 0; thus, we mark the length of the path next to the node.