Graph based Representation and Analysis of Text Document: A Survey of Techniques
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TLDR
The survey results shows that Graph based representation is appropriate way of representing text document and improved result of analysis over traditional model for different text applications.Abstract:
A common and standard approach to model text document is bag-of-words. This model is suitable for capturing word frequency, however structural and semantic information is ignored. Graph representation is mathematical constructs and can model relationship and structural information effectively. A text can appropriately represented as Graph using vertex as feature term and edge relation can be significant relation between the feature terms. Text representation using Graph model provides computations related to various operations like term weight, ranking which is helpful in many applications in information retrieval. This paper presents a systematic survey of existing work on Graph based representation of text and also focused on Graph based analysis of text document for different operations in information retrieval. In this process taxonomy of Graph based representation and analysis of text document is derived and result of different methods of Graph based text representation and analysis are discussed. The survey results shows that Graph based representation is appropriate way of representing text document and improved result of analysis over traditional model for different text applications.read more
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References
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Proceedings Article
TextRank: Bringing Order into Text
Rada Mihalcea,Paul Tarau +1 more
TL;DR: TextRank, a graph-based ranking model for text processing, is introduced and it is shown how this model can be successfully used in natural language applications.
Journal ArticleDOI
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Journal Article
LexRank: Graph-based Centrality as Salience in Text Summarization
Gunes Erkan,Dragomir R. Radev +1 more
TL;DR: A new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences is considered and the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank.
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Adilson E. Motter,Alessandro P. S. de Moura,Alessandro P. S. de Moura,Ying-Cheng Lai,Partha Dasgupta +4 more
TL;DR: This work maps out the conceptual network of the English language, with the connections being defined by the entries in a Thesaurus dictionary, and finds that this network presents a small-world structure, and appears to exhibit an asymptotic scale-free feature with algebraic connectivity distribution.
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