%0 Book %1 Mihalcea2011 %A Mihalcea, Rada %A Radev, Dragomir %C Cambridge; New York %D 2011 %I Cambridge University Press %K general_text_mining keyword_extraction networks %T Graph-based natural language processing and information retrieval %U http://www.amazon.com/gp/product/0521896134/ref=s9_psimh_gw_p14_d20_i2?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-2&pf_rd_r=0J12R3YZJ39NWZTPSW12&pf_rd_t=101&pf_rd_p=1688200382&pf_rd_i=507846 %X "This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval"-- "Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms"-- %@ 9780521896139 0521896134

@book{Mihalcea2011, abstract = {"This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval"-- "Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms"--}, added-at = {2014-07-01T11:13:15.000+0200}, address = {Cambridge; New York}, author = {Mihalcea, Rada and Radev, Dragomir}, biburl = {https://www.bibsonomy.org/bibtex/2cbaccc9f5a1151a65eafbeb445851e86/saos}, description = {Graph-based Natural Language Processing and Information Retrieval: Rada Mihalcea, Dragomir Radev: 9780521896139: Amazon.com: Books}, interhash = {ea3af276a07b03a49399fbd860f77d8d}, intrahash = {cbaccc9f5a1151a65eafbeb445851e86}, isbn = {9780521896139 0521896134}, keywords = {general_text_mining keyword_extraction networks}, publisher = {Cambridge University Press}, timestamp = {2014-07-01T11:30:50.000+0200}, title = {Graph-based natural language processing and information retrieval}, url = {http://www.amazon.com/gp/product/0521896134/ref=s9_psimh_gw_p14_d20_i2?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-2&pf_rd_r=0J12R3YZJ39NWZTPSW12&pf_rd_t=101&pf_rd_p=1688200382&pf_rd_i=507846}, year = 2011 }