Sci-Search: Academic Search and Analysis System Based on Keyphrases
Structured data representation allows saving much time during relevant information search and gives a useful view on a domain. It allows researchers to find relevant publications faster and getting insights about tendencies and dynamics of a particular scientific domain as well as finding emerging topics. Sorted lists of search results provided by the popular search engines are not suitable for such a task. In this paper we demonstrate a demo version of a search engine working with abstracts of scientific articles and providing structured representation of information to the user. Keyphrases are used as the basis for processing algorithms and representation. Some algorithm details are described in the paper. A number of test requests and their results are discussed.
KeywordsRepresenting search results academic search engine keyphrase extraction clustering indexing informational retrieval
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- 1.Li, Q., Bot, R.S., Chen, X.: Incorporating Document Keyphrases. In: The 10th Americas Conference on Information Systems, New York (2004)Google Scholar
- 3.Bernardini, A., Carpineto, C.: Full-Subtopic Retrieval with Keyphrase-Based Search Results Clustering. In: Web Intelligence and Intelligent Agent Technologies (2009)Google Scholar
- 5.Zeng, H.J., He, Q.C., Chen, Z., Ma, W.Y., Ma, J.: Learning to cluster web search results. In: The 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 210–217 (2004)Google Scholar
- 6.Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Conference on Empirical Methods in Natural Language Processing, pp. 216–223 (2003)Google Scholar
- 7.Mihalcea, R., Tarau, P.: TextRank: Bringing order into texts. In: Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)Google Scholar
- 8.Kim, S.N., Medelyan, O., Yen, M.: Automatic keyphrase extraction from scientific articles. Language Resources and Evaluation, Springer Kan & Timothy Baldwin (2012)Google Scholar
- 9.Xiaojun, W., Xiao, J.: Exploiting Neighborhood Knowledge for Single Document Summarization and Keyphrase Extraction ACM Transactions on Information Systems 28(2), Article 8 (2010)Google Scholar
- 10.Zesch, T., Gurevych, I.: Approximate Matching for Evaluating Keyphrase Extraction. In: International Conference RANLP 2009, Borovets, Bulgaria, pp. 484–489 (2009)Google Scholar
- 11.Popova, S., Khodyrev, I.: Ranking in keyphrase extraction problem: is it useful to use statistics of words occurrences? Proceedings of the Kazan University Journal (2013)Google Scholar