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 .
in @cite_7 present an early research which represents a document by a undirected and unweighted co-occurrence network, and study small-world properties of text. Based on the network topology, the authors proposed an indexing system called KeyWorld, which extracts important terms (pair of words) by measuring their contribution to small world properties. The contribution of the node is based on closeness centrality calculated as the difference in small world properties of the network with temporarily elimination of a node combined with inverse document frequency (idf).
@cite_7
88154610 1491844874 1550806611 1965667542
The small world topology is known widespread in biological, social and man-made systems. This paper shows that the small world structure also exists in documents,such as papers. A document is represented by a network;the nodes represent terms,and the edges represent the co-occurrence of terms. This network is shown to have the characteristics of being a small world,i.e.,nodes are highly clustered yet the path length between them is small. Based on the topology,we develop an indexing system called KeyWorld,which extracts important terms by measuring their contribution to the graph being small world. A document is represented by a network; the nodes represent terms, and the edges represent the co-occurrence of terms. This paper shows that the network has the characteristics of being small world, i.e., highly clustered and short path length. Based on the topology, we can extract important terms, even if they are rare, by measuring their contribution to the graph being small world. Keywords characterize the topics discussed in a document. Extracting a small set of keywords from a single document is an important problem in text mining. We propose a hybrid structural and statistical approach to extract keywords. We represent the given document as an undirected graph, whose vertices are words in the document and the edges are labeled with a dissimilarity measure between two words, derived from the frequency of their co-occurrence in the document. We propose that central vertices in this graph are candidates as keywords. We model importance of a word in terms of its centrality in this graph. Using graph-theoretical notions of vertex centrality, we suggest several algorithms to extract keywords from the given document. We demonstrate the effectiveness of the proposed algorithms on real-life documents. A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these “local” relevance decisions as the “document-wide” relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity - log p(rvt ∈ d) is interpreted as the probability of randomly picking a nonrelevant usage (denoted by r) of term t. Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections.