An Iterative Method for Enhancing Text Comprehension by Automatic Reading of References

Humans read references to increase understanding about a topic. In this research, we investigate an interesting algorithm which tries to mimic the human reading process. Given a free text about a topic, the algorithm iteratively discovers important references to illuminate the less understood part of the text at hand and then analyzes the reference text to add new knowledge paths. The algorithm also consults modern semantic dictionary through the analysis as needed. In this paper, we are going to share an experiment, which uses Wikipedia pages as reference and WordNet as the ontology engine to understand a news article. We display the knowledge gain via this algorithm. KeywordsWordNet; Wikipedia; Semantic-Graph; Illuiminated-Semantic-Graph.

[1]  Abdelmajid Ben Hamadou,et al.  Wikipedia Category Graph and New Intrinsic Information Content Metric for Word Semantic Relatedness Measuring , 2012, ICDKE.

[2]  Gene H. Golub,et al.  Singular value decomposition and least squares solutions , 1970, Milestones in Matrix Computation.

[3]  Dongsong Zhang,et al.  An integrated method for hierarchy construction of domain-specific terms , 2014, 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS).

[4]  Evangelos E. Milios,et al.  Tulip: lightweight entity recognition and disambiguation using wikipedia-based topic centroids , 2014, ERD '14.

[5]  Ei Ei Mon,et al.  Computing Semantic Relatedness using Wikipedia Taxonomy by Spreading Activation , 2014 .

[6]  Amal Babour,et al.  Tweet Sentiment Analytics with Context Sensitive Tone-Word Lexicon , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[7]  Ling Chen,et al.  WordNet Based Multi-Way Concept Hierarchy Construction from Text Corpus , 2013, AAAI.

[8]  Manas Hardas,et al.  SEGMENTATION AND INTEGRATION IN TEXT COMPREHENSION: A MODEL OF CONCEPT NETWORK GROWTH , 2012 .

[9]  Ling Chen,et al.  Automatic multi-way domain concept hierarchy construction from customer reviews , 2015, Neurocomputing.

[10]  Steffen Staab,et al.  Learning Concept Hierarchies from Text with a Guided Hierarchical Clustering Algorithm , 2005 .

[11]  Dan I. Moldovan,et al.  Automatic Discovery of Part-Whole Relations , 2006, CL.

[12]  Chandra Shekhar Yadav,et al.  Semantic graph based approach for text mining , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[13]  Christiane Fellbaum,et al.  Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms , 1998 .

[14]  Robert L. Grossman,et al.  Graph-Theoretic Scagnostics , 2005, INFOVIS.