Preliminary report on network based keyword extraction for Croatian is an unsupervised method for keyword extraction from the complex network. We build our approach with a new network measure the node selectivity, motivated by the research of the graph based centrality approaches. The node selectivity is defined as the average weight distribution on the links of the single node. We extract nodes (keyword candidates) based on the selectivity value. Furthermore, we expand extracted nodes to word-tuples ranked with the highest in out selectivity values. Selectivity based extraction does not require linguistic knowledge while it is purely derived from statistical and structural information en-compassed in the source text which is reflected into the structure of the network. Obtained sets are evaluated on a manually annotated keywords: for the set of extracted keyword candidates average F1 score is 24,63 , and average F2 score is 21,19 ; for the exacted words-tuples candidates average F1 score is 25,9 and average F2 score is 24,47 .
@cite_12 extract keywords and keyphrases form co-occurrence networks of words and from noun phrases collocations networks. Eleven measures (degree, strength, neighbourhood size, coreness, clustering coefficient, structural diversity index, page rank, HITS – hub and authority score, betweenness, closeness and eigenvector centrality) are used for keyword extraction from directed undirected and weighted networks. Obtained results on 4 data sets are suggesting that centrality measures outperform the baseline term frequency inverse document frequency (tf-idf) model, and simpler measures like degree and strength outperform computationally more expensive centrality measures like coreness and betweenness.
@cite_12
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Abstract As network analysis methods prevail, more metrics are applied to co-word networks to reveal hot topics in a field. However, few studies have examined the relationships among these metrics. To bridge this gap, this study explores the relationships among different ranking metrics, including one frequency-based and six network-based metrics, in order to understand the impact of network structural features on ranking themes on co-word networks. We collected bibliographic data from three disciplines from Web of Science (WoS), and generated 40 simulation networks following the preferential attachment assumption. Correlation analysis on the empirical and simulated networks shows strong relationships among the metrics. Their relationships are consistent across disciplines. The metrics can be categorized into three groups according to the strength of their correlations, where Degree Centrality, H-index, and Coreness are in one group, Betweenness Centrality, Clustering Coefficient, and frequency in another, and Weighted PageRank by itself. Regression analysis on the simulation networks reveals that network topology properties, such as connectivity, sparsity, and aggregation, influence the relationships among selected metrics. In addition, when comparing the top keywords ranked by the metrics in the three disciplines, we found the metrics exhibit different discriminative capacity. Coreness and H-index may be better suited for categorizing keywords rather than ranking keywords. Findings from this study contribute to a better understanding of the relationships among different metrics and provide guidance for using them effectively in different contexts. Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variants of PageRank algorithm on a network of words. Although graph-based approaches are knowledge-lean and easily adoptable in online systems, it remains largely open whether they can benefit from centrality measures other than PageRank. In this paper, we experiment with an array of centrality measures on word and noun phrase collocation networks, and analyze their performance on four benchmark datasets. Not only are there centrality measures that perform as well as or better than PageRank, but they are much simpler (e.g., degree, strength, and neighborhood size). Furthermore, centrality-based methods give results that are competitive with and, in some cases, better than two strong unsupervised baselines. We use measures of vertex centrality to examine interlocking directorates and their economic effects in Italy. We employ centrality measures like degree, eigenvector centrality, betweenness, and flow betweenness, along with the clustering coefficient. We document the existence of a negative relationship between both degree and eigenvector centrality and firm value. Betweenness and flow betweenness, on the other hand, are not associated with lower firm valuations. We argue that these differences derive from the different properties of these measures: while degree and eigenvector centrality measures the influence and the power of the connections, betweenness and flow betweenness are proxies for the volume of information that passes between the nodes. This result is robust with respect to the use of both stock market and operating performance measures, as well as several controlling variables. In this paper, we present and compare various centrality measures for graphbased keyphrase extraction. Through experiments carried out on three standard datasets of different languages and domains, we show that simple degree centrality achieve results comparable to the widely used TextRank algorithm, and that closeness centrality obtains the best results on short documents.