Agirre, E., and A., Soroa. 2009. Personalizing PageRank for word-sense disambiguation. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, pp. 33–41, Athens, Greece.Google Scholar

Aho, A. V., and N. J. A., Sloane. 1973. Some doubly exponential sequences. Fibonacci Quarterly11.Google Scholar

Aho, A. V., S.-F., Chang, K., McKeown, D. R., Radev, J. R., Smith, and K. A., Zaman. 1997. Columbia digital news project: An environment for briefing and search over multimedia information. Int. J. on Digital Libraries 1(4):377–385.CrossRefGoogle Scholar

Amancio, D. R., L., Antiqueira, T., Pardo, L., daF., Costa, O., Oliveira, and M., dasG. V., Nunes. 2008. Complex networks analysis of manual and machine translations. International Journal of Modern Physics, C 19:583–598.CrossRefGoogle Scholar

Antiqueira, L., M. G. V., Nunes, and O. C., Oliveira Jr. 2007. Strong correlations between text quality and complex networks features. Physica, A 373:811–820.CrossRefGoogle Scholar

Antiqueira, L., O. N., Oliveira Jr., L., daF., Costa, and M., dasG. V., Nunes. 2009. A complex network approach to text summarization. Information Sciences, 179:584–599.CrossRefGoogle Scholar

Baliński, J., and C., Danilowicz. 2005. Re-ranking method based on inter-document distances. Information Processing and Management 41:759–775.CrossRefGoogle Scholar

Barabasi, A. L. 2003. Linked: How Everything Is Connected to Everything Else and What It Means. New York: Plume.Google Scholar

Barabasi, A. L., and R., Albert. 1999. Emergence of scaling in random networks. Science, 286:509–512.Google ScholarPubMed

Barrat, A., and M., Weigt 2000. On the properties of small-world network models. The European Physical Journal, B 13:547–560.CrossRefGoogle Scholar

Barthélémy, M., and L. A., Nunes Amaral. 1999. Small-world networks: Evidence for a crossover picture. Physical Reviews Letters 82:3180–3183.CrossRefGoogle Scholar

Batagelj, V., and A. M., Pajek. 2003. Analysis and visualization of large networks. In Junger, M., and P., Mutzel (Eds.), Graph Drawing Software, pp. 77–103, Berlin: Springer.Google Scholar

Biemann, C. 2006a. Chinese Whispers: An efficient graph-clustering algorithm and its application to natural language processing problems. In Proceedings of TextGraphs: The Second Workshop on Graph-Based Methods for Natural Language Processing, pp. 73–80, New York City, Association for Computational Linguistics.CrossRefGoogle Scholar

Biemann, C. 2006b. Unsupervised part-of-speech tagging employing efficient graph clustering. In Proceedings of the COLING/ACL 2006 Student Research Workshop, pp. 7–12, Sydney, Australia, Association for Computational Linguistics.Google Scholar

Biemann, C.I., Matveeva, R., Mihalcea, and D., Radev (eds.). 2007. Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing. Rochester, NY, USA, Association for Computational Linguistics.Google Scholar

Biemann, C., and S., Teresniak. 2005. Disentangling from Babylonian confusion: Unsupervised language identification. In Proceedings of CICLing-2005, pp. 762–773, Mexico City, Mexico.Google Scholar

Blum, A., and S., Chawla. 2001. Learning from labeled and unlabeled data using graph min-cuts. International Conference on Machine Learning, pp. 19–26.Google Scholar

Bollobás, B. 1985. Random Graphs. London, UK: Academic Press.Google Scholar

Bollobás, B. 1998. Modern Graph Theory. New York: Springer.CrossRefGoogle Scholar

Bramsen, P.P., Deshpande, Y. K., Lee, and R., Barzilay. 2006. Inducing temporal graphs. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 189–198, Sydney, Australia, Association for Computational Linguistics.Google Scholar

Brin, S., and L., Page. 1998. The anatomy of a large-scale hypertextual Web search engine. In Computer Networks and ISDN Systems30.Google Scholar

Budanitsky, A., and G., Hirst. 2006. Evaluating WordNet-based measures of semantic distance. Computational Linguistics 32:13–47.CrossRefGoogle Scholar

Burr, S. A., B., Grunbaum, and N. J. A., Sloane. 1974. “The Orchard Problem.” Geometriae dedicata 2:397–424.CrossRefGoogle Scholar

Caldeira, S. M. G., T. C., Petit Lobao, R. F. S., Andrade, A., Neme, and J. G. V., Miranda 2006. The network of concepts in written texts. European Physical Journal, B 49:523–529.CrossRefGoogle Scholar

Carbonell, J. G., and J., Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336, Melbourne, Australia.Google Scholar

Banea, C. A.Moschitti, S.Somasundaran, and F. M., Zanzotto (eds.). 2010. Annual Meeting of the Association for Computational Linguistics 2010: Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing. Uppsala, Sweden.Google Scholar

Chen, J., D., Ji, C. L., Tan, and Z., Niu. 2006. Relation extraction using label-propagation–based semi-supervised learning. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 129–136, Sydney, Australia, Association for Computational Linguistics.Google Scholar

Chowdhury, M., S., Hassan, A., Mukherjee, and S., Muresan (eds.), 2009. Annual Meeting of the Association for Computational Linguistics 2009: Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing. Singapore, Association for Computational Linguistics.Google Scholar

Chu, Y. J., and T. H., Liu. 1965. On the shortest arborescence of a directed graph. Science Sinica 14:1396–1400.Google Scholar

Clauset, A.C. R., Shalizi, and M. E. J., Newman. 2009. Power-law distributions in empirical data. SIAM Review 51:661–703.CrossRefGoogle Scholar

Collins, A. M., and E., Loftus. 1975. A spreading-activation theory of semantic processing. Psychological Review 82:407–428.CrossRefGoogle Scholar

Collins-Thompson, K., and J., Callan. 2005. Query expansion using random-walk models. In CIKM '05: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 704–711, New York, USA.CrossRefGoogle Scholar

Cook, D., and L., Holder, 2006. Mining Graph Data. John Wiley & Sons.CrossRefGoogle Scholar

Coursey, K., R., Mihalcea, and W., Moen, 2009. Using encyclopedic knowledge for automatic topic identification. In Proceedings of the Conference on Natural Language Learning, pp. 210–218, Boulder, CO, USA.Google Scholar

Dagan, I., O., Glickman, and B., Magnini. 2005. The PASCAL recognising textual entailment challenge. In Proceedings of the PASCAL Workshop, LNCS 3944, pp. 177–190, Springer.Google Scholar

De Wit, D. 2000. Partitioning sparse graphs using the second eigenvector of their graph Laplacian. Technical Report math/0003036, available at arXiv.org.

Dhillon, I. S. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. pp. 269–274, San Francisco, CA: ACM SIGKDD.Google Scholar

Dias, G., C., Santos, and G., Cleuziou. 2006. Automatic knowledge representation using a graph-based algorithm for language-independent lexical chaining. In Proceedings of the Workshop on Information Extraction Beyond the Document, pp. 36–47, Sydney, Australia, Association for Computational Linguistics.Google Scholar

Diestel, R. 2005. Graph Theory. Springer.Google Scholar

Dodds, P. S., R., Muhamad, and D. J., Watts. 2003. An experimental study of search in global social networks. Science 301:827–629.CrossRefGoogle ScholarPubMed

Dorogovtsev, S. N., and J. F. F., Mendes. 2001. Language as an evolving word web. Proceedings of the Royal Society of London, B 268:2603–2606.CrossRefGoogle ScholarPubMed

Doyle, P. G., and J. L., Snell, 1984. Random walks and electric networks. Technical Report math.PR/0001057, available at arXiv.org.

Dunning, T. E. 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19:61–74.Google Scholar

Edmonds, J. 1967. Optimum branchings. Journal of Research of the National Bureau of Standards 71B:233–240.CrossRefGoogle Scholar

Edmunson, H. P. 1969. New methods in automatic extracting. Journal of the ACM, 16:264–285.CrossRefGoogle Scholar

Erdos, P., and B., Grunbaum. 1973. Osculation vertices in arrangements of curves. Geometriae dedicata 1:322–333.CrossRefGoogle Scholar

Erkan, G. 2006. Language model-based document clustering using random walks. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, New York, USA.Google Scholar

Erkan, G., and D., Radev. 2004a. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research 22:457–479.Google Scholar

Erkan, G., and D., Radev. 2004b. The University of Michigan at DUC 2004. In Document Understanding Conference (DUC), Boston, MA, USA.Google Scholar

Esuli, A., and F., Sebastiani. 2007. PageRanking WordNet synsets: An application to opinion mining. In Proceedings of the Annual Meeting of the Association of Computational Linguistics, pp. 424–431, Prague, Czech Republic.Google Scholar

Ferrer-i-Cancho, R., A., Mehler, O., Pustylnikov, and A., Díaz-Guilera. 2007. Correlations in the organization of large-scale syntactic dependency networks. In TextGraphs: Graph-Based Algorithms for Natural Language Processing, pp. 65–72, Rochester, NY, USA, Association for Computational Linguistics.Google Scholar

Ferrer-i-Cancho, R., and R. V., Sole. 2001. The small world of human language. Proceedings of the Royal Society of London, B, Biological Sciences 268:2261–2265.CrossRefGoogle ScholarPubMed

Ferrer-i-Cancho, R., R. V., Solé, and R., Köhler. 2004. Patterns in syntactic dependency networks. Physical Review, E 69:050915.CrossRefGoogle ScholarPubMed

Filippova, K., and M., Strube. 2008. Sentence fusion via dependency graph compression. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 177–185, Honolulu, Hawaii, USA.Google Scholar

Fodor, J., and Z., Pylyshyn. 1988. Connectionism and cognitive architecture: A critical analysis. In S., Pinker and J., Mehler (eds.), Connections and Symbols. Cambridge, MA: MIT Press.Google Scholar

Frank, E., G. W., Paynter, I. H., Witten, C., Gutwin, and C., Nevill-Manning. 1999. Domain-specific keyphrase extraction. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 668–673, Stockholm.Google Scholar

Freud, S. 1901. Psychopathology of Everyday Life. Payot.Google Scholar

Fürstenau, H., and M., Lapata. 2009. Graph alignment for semi-supervised semantic role labeling. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 11–20, Singapore.Google Scholar

Gamon, M. 2006. Graph-based text representation for novelty detection. In Proceedings of TextGraphs: The Second Workshop on Graph-Based Methods for Natural Language Processing, pp. 17–24, New York, USA, Association for Computational Linguistics.CrossRefGoogle Scholar

Girvan, M., and M., Newman. 2002. Community structure in social and biological networks. In Proceedings of the National Academy of Sciences, pp. 7821–7826.CrossRefGoogle ScholarPubMed

Goldberg, A., and X., Zhu. 2006. Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization. In Proceedings of TextGraphs: The Second Workshop on Graph-Based Methods for Natural Language Processing pp. 45–52, New York, USA.CrossRefGoogle Scholar

Granovetter, M. S. 1973. The strength of weak ties. The American Journal of Sociology 78:1360–1380.CrossRefGoogle Scholar

Grimmett, G. 1999. Percolation, second edition, volume 321 of Grundlehren der mathematischen Wissenschaften. Springer.Google Scholar

Grimmett, G., and D., Stirzaker. 1989. Probability and Random Processes. Oxford University Press.Google Scholar

Gross, J., and J., Yellen. 2005. Graph Theory and Its Applications. Chapman and Hall.Google Scholar

Haghighi, A., A., Ng, and C., Manning, 2005. Robust textual inference via graph matching. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 387–394.Google Scholar

Hassan, H., A., Hassan, and O., Emam, 2006. Unsupervised information extraction approach using graph mutual reinforcement. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 501–508, Prague, Czech Republic.Google Scholar

Hassan, S., R., Mihalcea, and C., Banea. 2007. Random-walk term weighting for improved text classification. In Proceedings of the International Conference on Semantic Computing, Irvine, CA, USA.Google Scholar

Hatzivassiloglou, V., and K., McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, pp. 174–181, Madrid, Spain.Google Scholar

Haveliwala, T. 2002. Topic-sensitive PageRank. In Proceedings of the Eleventh International World Wide Web Conference, pp. 517–526, Honolulu, Hawaii.CrossRefGoogle Scholar

Hearst, M. A. 1994. Multi-paragraph segmentation of expository text. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, USA.Google Scholar

Herings, P. J., G., van der Laan, and D., Talman, 2001. Measuring the power of nodes in digraphs. Technical Report, Tinbergen Institute.

Hirst, G. 1988. Resolving lexical ambiguity computationally with spreading activation and Polaroid words. In S., Small, G., Cottrell, and M., Tanenhaus (eds.), Lexical Ambiguity Resolution. Morgan Kaufmann.Google Scholar

Hovy, E., L., Gerber, U., Hermjakob, C. Y., Lin, and D., Ravichandran, 2001. Toward semantics-based answer pinpointing. In Proceedings of the Human Language Technology Conference, San Diego, CA, USA.Google Scholar

Hudson, R. A. 1984. Word Grammar. Oxford, UK: Blackwell.Google Scholar

Hughes, T., and D., Ramage. 2007. Lexical semantic relatedness with random graph walks. In Proceedings of Conference on Empirical Methods in Natural Language Processing, Prague, Czech Republic.Google Scholar

Hulth, A. 2003. Improved automatic keyword extraction given more linguistic knowledge. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Sappora, Japan.Google Scholar

Jackson, M. 2009. Social and Economic Networks. Princeton, NJ: Princeton University Press.Google Scholar

Jarmasz, M., and S., Szpakowicz. 2003. Roget's Thesaurus and semantic similarity. In Proceedings of the Conference on Recent Advances in Natural Language Processing, Borovetz, Bulgaria.Google Scholar

Jiang, J., and D., Conrath. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the International Conference on Research in Computational Linguistics, Taiwan.Google Scholar

Kamps, J., M., Marx, R. J., Mokken, and M., de Rijke. 2004. Using WordNet to measure semantic orientation of adjectives. In Proceedings of the Fourth International Conference on Language Resources and Evaluation, Lisbon, Portugal.Google Scholar

Kamvar, S., T., Haveliwala, C., Manning, and G., Golub. 2003. Extrapolation methods for accelerating PageRank computations. In Proceedings of the 12th International World Wide Web Conference, Budapest, Hungary.Google Scholar

Kernighan, B., and S., Lin. 1970. An effective heuristic procedure for partitioning graphs. The Bell System Technical Journal291–308.CrossRefGoogle Scholar

Kleinberg, J. M. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM 46:604–632.CrossRefGoogle Scholar

Kleinberg, J., and D., Easley. 2009. Networks, Crowds, and Markets. New York: Cambridge University Press.Google Scholar

Kozareva, Z., E., Riloff, and E., Hovy. 2008. Semantic class learning from the Web with hyponym pattern linkage graphs. In Proceedings of the Association for Computational Linguistics, pp. 1048–1056, Columbus, OH, USA.Google Scholar

Kullback, S. 1987. Letter to the editor: The Kullback-Leibler distance. The American Statistician 41:340–341.Google Scholar

Kurland, O., and L., Lee. 2004. Corpus structure, language models, and ad hoc information retrieval. In Proceedings of the 27th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 194–201, Manchester, UK.Google Scholar

Kurland, O., and L., Lee. 2005. PageRank without hyperlinks: Structural reranking using links induced by language models. In Proceedings of the Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 306–313.Google Scholar

Kurland, O., and L., Lee. 2006. Respect my authority! HITS without hyperlinks, utilizing cluster-based language models. In Proceedings of the Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 83–90.Google Scholar

Lancichinetti, A., and S., Fortunato. 2009. Community detection algorithms: A comparative analysis. Physical Review E 80, 056117.CrossRefGoogle ScholarPubMed

Lanczos, C. 1950. An iteration method for the solution of the eigenvalue problem of linear differential and integral operators. J. Res. Nat. Bur. Stand., 35, 255–282.CrossRefGoogle Scholar

Leacock, C., and M., Chodorow. 1998. Combining local context and WordNet sense similarity for word-sense identification. In WordNet, An Electronic Lexical Database. Cambridge, MA: The MIT Press.Google Scholar

Lee, L. 1997. Similarity-Based Approaches to Natural Language Processing. Ph.D. thesis, Harvard University, Cambridge, MA.

Lin, D. 1998. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning, Madison, WI, USA.Google Scholar

Luhn, H. P. 1958. The automatic creation of literature abstracts. IBM Journal of Research and Development 2:159–165.CrossRefGoogle Scholar

Malioutov, I., and R., Barzilay. 2006. Minimum-cut model for spoken lecture segmentation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 25–32, Sydney, Australia.Google Scholar

Manning, C., and H., Schütze. 1999. Foundations of statistical Natural Language Processing, MIT Press, Cambridge, CA.Google Scholar

Manning, C., and P., Raghavan and H., Schütze. 2008. Introduction to Information Retrieval, Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar

Marcu, D. 2000. The Theory and Practice of Discourse Parsing and Summarization. Cambridge, MA: The MIT Press.Google Scholar

Masucci, A. P., and G. J., Rodgers. 2006. Network properties of written human language. Physical Review, E 74(2):026102.CrossRefGoogle ScholarPubMed

Matveeva, I., C., Biemann, M., Choudhury, and M., Diab, (eds.). 2008. International Conference on Computational Linguistics 2008: Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing. Manchester, UK.Google Scholar

McCallum, A., X., Wang, and A., Corrada-Emmanuel. 2007. Topic and role discovery in social networks with experiments on ENRON and academic email. Journal of Artificial Intelligence Research 30:249–272.Google Scholar

McDonald, R., F., Pereira, K., Ribarov, and J., Hajic, 2005. Nonprojective dependency parsing using spanning-tree algorithms. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 523–530, Vancouver, BC, Canada.CrossRefGoogle Scholar

Medelyan, O. 2007. Computing lexical chains with graph clustering. In Proceedings of the Annual Meeting of the Association for Computational Linguistics 2007 Student Research Workshop, pp. 85–90, Prague, Czech Republic, Association for Computational Lingustics.Google Scholar

Medelyan, O., and I. H., Witten. 2008. Topic indexing with Wikipedia. In Proceedings of the AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 14–24, Chicago, USA.Google Scholar

Mehler, A. 2007. Large text networks as an object of corpus linguistic studies. In Corpus Linguistics: An International Handbook. Berlin/New York: de Gruyter.Google Scholar

Mihalcea, R. 2004. Graph-based ranking algorithms for sentence extraction, applied to text summarization. In Proceedings of the 42nd Annual Meeting of the Association for Computational Lingusitics (companion volume), pp. 170–173, Barcelona, Spain.Google Scholar

Mihalcea, R. 2005. Large vocabulary unsupervised word-sense disambiguation with graph-based algorithms for sequence data labeling. In Proceedings of the Human Language Technology/Empirical Methods in Natural Language Processing Conference, Vancouver, BC, Canada.Google Scholar

Mihalcea, R., and Ceylan, H. 2007. Explorations in automatic book summarization. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic.Google Scholar

Mihalcea, R., C., Corley, and C., Strapparava, 2006. Corpus-based and knowledge-based approaches to text semantic similarity. In Proceedings of the American Association for Artificial Intelligence, pp. 775–780, Boston, MA, USA.Google Scholar

Mihalcea, R., and A., Csomai. 2007. Wikify! Linking documents to encyclopedic knowledge. In Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 233–242, Lisbon, Portugal.CrossRefGoogle Scholar

Mihalcea, R., and D., Radev, (eds.). 2006. Proceedings of TextGraphs: The Second Workshop on Graph-Based Methods for Natural Language Processing. New York: Association for Computational Linguistics.Google Scholar

Mihalcea, R., and P., Tarau. 2004. TextRank: Bringing order into texts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 404–411, Barcelona, Spain.Google Scholar

Mihalcea, R., P., Tarau, and E., Figa. 2004. PageRank on semantic networks, with application to word-sense disambiguation. In Proceedings of the 20th International Conference on Computational Linguistics, pp. 1126–1132, Geneva, Switzerland.Google Scholar

Miller, George. 1995. WordNet: A lexical database. Communication of the ACM 38:39–41.CrossRefGoogle Scholar

Miller, G., and W., Charles. 1998. Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1):1–28.CrossRefGoogle Scholar

Minkov, E., and W., Cohen. 2008. Learning graph walk-based similarity measures for parsed text. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 907–916, Honolulu, Hawaii, USA.Google Scholar

Minkov, E., W., Cohen, and A., Ng. 2006. Contextual search and name disambiguation in email using graphs. In Proceedings of the International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 27–34, Seattle, WA, USA.Google Scholar

Molla, D. 2006. Learning of graph-based question-answering rules. In Proceedings of TextGraphs: The Second Workshop on Graph-Based Methods for Natural Language Processing, pp. 37–44, New York City, Association for Computational Linguistics.CrossRefGoogle Scholar

Monz, C., and B., Dorr. 2005. Iterative translation disambiguation for cross-language information retrieval. In Proceedings of the 28th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 520–527, Salvador, Brazil.Google Scholar

Motter, A. E., A. P. S., de Moura, Y. C., Lai, and P., Dasgupta, 2002. Topology of the conceptual network of language. Physical Review E 65(6):065102.CrossRefGoogle ScholarPubMed

Myaeng, S. H., and A., Lopez–Lopez. 1992. Conceptual graph matching: A flexible algorithm and experiments. Journal of Experimental and Theoretical Artificial Intelligence 4:107–126.CrossRefGoogle Scholar

Nastase, V., and S., Szpakowicz. 2006. Matching syntactic-semantic graphs for semantic relation assignment. In Proceedings of TextGraphs: The Second Workshop on Graph-Based Methods for Natural Language Processing, pp. 81–88, New York, Association for Computational Linguistics.CrossRefGoogle Scholar

Navigli, R., and M., Lapata. 2007. Graph connectivity measures for unsupervised wordsense disambiguation. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 1683–1688, Hyderabad, India.Google Scholar

Nelson, D. L., C. L., McEvoy, and T. A., Schreiber. 1999. The University of South Florida word association norms.http://www.usf.edu/Free Association.

Newman, M. 2002. Associative mixing in networks, Physics Rev. Letter 89, 20870.CrossRefGoogle Scholar

Newman, M. E. J. 2002. Random graphs as models of networks. Available at http://arxiv.org/abs/cond-mat/0202208.

Newman, M. E. J. 2010. Networks: An Introduction. New York: Oxford University Press.CrossRefGoogle Scholar

Newman, M., A. L., Barabasi, and D. J., Watts. 2006. The Structure and Dynamics of Networks. Princeton, NJ: Princeton University Press.Google Scholar

Ney, H., S., Ortmanns, and I., Lindam. 1997. Extensions to the word graph method for large vocabulary continuous speech recognition. In Proceedings of ICASSP, volume 3, pp. 1791–1794, Munich, Germany.Google Scholar

Newman, M. 2010. Networks: An Introduction Oxford University Press, Oxford, UK.

Ng, A., A., Zheng, and M., Jordan. 2001. Stable algorithms for link analysis. In Proceedings of the 24th Annual International ACM/SIGIR, Conference on Research and Development in Information Retrieval, pp. 258–266, New Orleans, LA, USA.Google Scholar

Ng, V. 2009. Graph-cut based–anaphoricity determination for co-reference resolution. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 575–583, Boulder, CO, USA.

Nicolae, C., and G., Nicolae. 2006. Bestcut: A graph algorithm for co-reference resolution. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 275–283, Sydney, Australia.Google Scholar

Niu, Z. Y., D. H., Ji, and C. L., Tan. 2005. Word-sense disambiguation using label propagation based semi-supervised learning. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp. 395–402, Ann Arbor, MA, USA.Google Scholar

Nivre, J., and R., McDonald. 2008. Integrating graph-based and transition-based dependency parsers. In Proceedings of the Association for Computational Linguistics, pp. 950–958, Columbus, OH, USA.Google Scholar

Orwell, G. 1949. Nineteen eighty-four. London: Secker and Warburg.Google Scholar

Otterbacher, J., G., Erkan, and D., Radev. 2005. Using randomwalks for question-focused sentence retrieval. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 915–922, Vancouver, BC, Canada.CrossRefGoogle Scholar

Pajek, , Program for large Network Analysis, 2010, http://pajek.imfm.si.

Pang, B., and L., Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics, pp. 271–278, Barcelona, Spain.Google Scholar

Pardo, T. A. S., Antiqueira, M. G. V.Nunes, O.N., Oliveira Jr. and L., daF., Costa. 2006. Modeling and evaluating summaries using complex networks. In VII PROPOR, Itatiaia, R100 de Janeito, Brasil.Google Scholar

Patel, A., and D., Radev. 2006. Lexical similarity can distinguish between automatic and manual translations. In Conference on Language Resources and Evaluations, pp. 1230–1235, Genoa, Italy.Google Scholar

Price, Solla. 1965. Networks of scientific papers. Science 149(3683):510–515.CrossRefGoogle ScholarPubMed

Quillian, M. 1968. Semantic memory. In M., Minsky (ed.), Semantic Information Processing. Cambridge, MA: The MIT Press.Google Scholar

Radev, D. 2004. Weakly supervised graph-based methods for classification. Technical Report CSE-TR-500-04, University of Michigan. Department of Electrical Engineering and Computer Science, Ann Arbor, MI, USA.

Radev, D., M., Hodges, A., Fader, M., Joseph, J., Gerrish, M., Schaller, J., dePeri, and B., Gibson. 2007. CLAIRLIB Documentation v1.03. 124: CSE-TR-536-07. Department of Electrical Engineering and Computer Science, University of Michigan. Available at http://clair.si.umich.edu/∼radev/papers/csetr536-07.pdf.

Raghavan, S., and H., Garcia-Molina. 2003. Representing Web graphs. In Proceedings of the IEEE International Conference on Data Engineering, pp. 1–10, Bangalore, India.Google Scholar

Ramage, D., A., Rafferty, and C., Manning. 2009. Random walks for text semantic similarity. In ACL-IJCNLP TextGraphs-4 Workshop, pp. 23–31, Suntec, Singapore.Google Scholar

Resnik, P. 1995. Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453, Montreal, Canada.Google Scholar

Richardson, M., and Domingos, P. 2002. The intelligent surfer: Probabilistic combination of link and content information in PageRank. In Advances in Neural Information Processing Systems, pp. 1441–1448, Vancouver, BC, Canada.Google Scholar

Riloff, E., and R., Jones. 1999. Learning dictionaries for information extraction by multilevel bootstrapping. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pp. 474–479, Orlando, FL, USA.Google Scholar

Rus, V., Graesser, A., and Desai, K. 2005. Lexico-syntactic subsumption for textual entailment. In Recent Advances in Natural Language Processing, pp. 187–196, Borovets, Bulgaria.Google Scholar

Salton, G.J., Allan, C., Buckley, and A., Singhal. 1994. Automatic analysis, theme generation, and summarization of machine-readable texts. Science 264:1421–1426.CrossRefGoogle ScholarPubMed

Salton, G., A., Singhal, M., Mitra, and C., Buckley. 1997. Automatic text structuring and summarization. Information Processing and Management 33:193–207.CrossRefGoogle Scholar

Schvaneveldt, R. 1989. Pathfinder Associative Networks: Studies in Knowledge Organization. New York: Ablex Publishing.Google Scholar

Shi, J., and J., Malik. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22:888–905.Google Scholar

Sigman, M., and G. A., Cecchi. 2002. Global organization of the WordNet lexicon. Proceedings of the National Academy of Sciences of the United States of America 99:1742–1747.CrossRefGoogle ScholarPubMed

Sinha, R., and R., Mihalcea. 2007. Unsupervised graph-based word-sense disambiguation using measures of word semantic similarity. In Proceedings of the IEEE International Conference on Semantic Computing, Irvine, CA, USA.Google Scholar

Sloane, N. Y. A. 1973. The persistence of a member. Journal of Recreational Math 6, pp. 97–98.Google Scholar

Soubbotin, M., and S., Soubbotin. 2001. Patterns of potential answer expressions as clues to the right answer. In Proceedings of the TREC-10 Conference, pp. 175–182, Gaithersburg, MD, USA.Google Scholar

Sowa, J. 1979. Semantics of conceptual graphs. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 39–44, La Jolla, CA, USA.Google Scholar

Sowa, J. 1983. Conceptual Structures: Information Processing in Mind and Machine. Boston: Addison-Wesley.Google Scholar

Spitzer, M. 1999. The Mind within the Net: Models of Learning, Thinking, and Acting. Cambridge, MA: The MIT Press.Google Scholar

Steyvers, M., and J. B., Tenenbaum. 2005a. Graph theoretic analyses of semantic networks: Small worlds in semantic networks. Cognitive Science 29:41–78.CrossRefGoogle Scholar

Steyvers, M., and J. B., Tenenbaum. 2005b. The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science 29:41–78.CrossRefGoogle Scholar

Stone, P. 2000. The general inquirer. Available at http://www.wjh.harvard.edu/inquirer/spreadsheet_guide.htm.

Su, F., and K., Markert. 2009. Subjectivity recognition on word senses via semisupervised min-cuts. In Proceedings of the Human Language Technology/North American Chapter of the Association for Computational Linguistics, pp. 1–9, Boulder, CO, USA.Google Scholar

Syed, Z., T., Finin, and A., Joshi. 2008. Wikipedia as an ontology for describing documents. In Proceedings of the Second International Conference on Weblogs and Social Media, pp. 136–144, Seattle, WA, USA.Google Scholar

Toutanova, K. C.D., Manning, and A. Y., Ng. 2004. Learning random walk models for inducing word-dependency distributions. In Proceedings of the 21st International Conference on Machine Learning, pp. 103–110, New York.Google Scholar

Travers, J., and S., Milgram. 1969. An experimental study of the small-world problem. Sociometry 32:425–443.CrossRefGoogle Scholar

Tsatsaronis, G., M., Vazirgiannis, and I., Androutsopoulos. 2007. Word-sense disambiguation with spreading activation networks generated from thesauri. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 1725–1730, Hyderabad, India.Google Scholar

Turney, P. 1999. Learning to extract keyphrases from text. Technical Report, National Research Council, Institute for Information Technology, Ottawa, ON, Canada.

Ueffing, N.F., Och, and H., Ney. 2002. Generation of word graphs in statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 156–163, Philadelphia, PA, USA.Google Scholar

Vanderwende, L.M., Banko, and A., Menezes. 2004. Event-centric summary generation. In Document Understanding Conference, Gaithersburg, MD.Google Scholar

Veronis, J., and N., Ide. 1990. Word-sense disambiguation with very large neural networks extracted from machine-readable dictionaries. In Proceedings of the 13th International Conference on Computational Linguistics, Helsinki, Finland.Google Scholar

Voorhees, E. M., and D. M., Tice. 2000. Building a question-answering test collection. In Proceedings of the 23rd Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 200–207, Athens, Greece.Google Scholar

Watts, D. 1999. Small Worlds, Princeton, NJ: Princeton, University Press.Google Scholar

Watts, D. J., and S. H., Strogatz. 1998. Collective dynamics of “small-world” networks. Nature 393(6684):409–410.CrossRefGoogle ScholarPubMed

Weale, T., C., Brew, and E., Fosler-Lussier. 2009. Using the Wiktionary graph structure for synonym detection. In Proceedings of the Workshop on the People's Web Meets NLP: Collaboratively Constructed Semantic Resources, pp. 28–31, Suntec, Singapore.CrossRefGoogle Scholar

Wei, Y., and C., Cheng. 1989. Towards efficient hierarchical designs by ratio cut partitioning. In Proceedings of the IEEE International Conference on Computer-Aided Design, pp. 298–301, Santa Clara, CA, USA.Google Scholar

Wei, Y., and C., Cheng. 1990. A two-level two-way partitioning algorithm. In Proceedings of the IEEE International Conference on Computer-Aided Design, pp. 516–519, Santa Clara, CA, USA.Google Scholar

White, H. D., and B. C., Griffith. 1981. Author co-citation: A literature measure of intellectual structure. Journal of American Society of Information Sciences 32(3):163–171.CrossRefGoogle Scholar

Widdows, D., and B., Dorow. 2002. A graph model for unsupervised lexical acquisition. In Proceedings of the 19th International Conference on Computational Linguistics, Taipei, Taiwan.Google Scholar

Wiebe, J., and R., Mihalcea. 2006. Word sense and subjectivity. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 1065–1072, Sydney, Australia.Google Scholar

Wolf, F., and E., Gibson. 2004a. Paragraph-, word-, and coherence-based approaches to sentence ranking: A comparison of algorithm and human performance. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain.Google Scholar

Wolf, F., and E., Gibson. 2004b. Representing discourse coherence: A corpus-based analysis. In Proceedings of the 20th International Conference on Computational Linguistics, pp. 134–140, Geneva, Switzerland.Google Scholar

Wu, Z., and M., Palmer. 1994. Verb semantics and lexical selection. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138 Las Cruces, NM, USA.CrossRefGoogle Scholar

Yeh, E., D., Ramage, C., Manning, E., Agirre, and A., Soroa. 2009. Wikiwalk: Random walks on Wikipedia for semantic relatedness. In ACL-IJCNLP TextGraphs-4 Workshop, pp. 41–49, Suntec, Singapore.Google Scholar

Zanette, D., and M., Montemurro. 2003. A stochastic model of text generation with realistic Zipf's distribution. Phys. Rev. E 67:031105.Google Scholar

Zanzotto, F., and A., Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. In Proceedings of the International Conference on Computational Linguistics and the Association for Computational Linguistics, pp. 401–408, Sydney, Australia.Google Scholar

Zens, R., and H., Ney. 2005. Word graphs for statistical machine translation. In Proceedings of the Anuual Meeting of the Association for Computational Linguistics Workshop on Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, pp. 191–198, Ann Arbor, MI, USA.Google Scholar

Zang, B., H., Li, Y., Liu, L., Ji, W., Xi, W., Fan, Z., Chen, and W. Y., Ma. 2005. Improving Web search results using affinity graph, Proceedings of the 28th ACM Conference on Research and Development in Information Retrieval, pp. 504–511, Salvador, Brazil.Google Scholar

Zhang, Y., and S., Clark. 2008. A tale of two parsers: Investigating and combining graphbased and transition-based dependency parsing using beam-search. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 562–571, Honolulu, Hawaii, USA.Google Scholar

Zhao, Y., and G., Karypis. 2001. Criterion functions for document clustering: Experiments and analysis. Technical Report TR 01-40, Department of Computer Science, University of Minnesota, Twin Cities, MN, USA.

Zhu, X. 2006. Semi-supervised learning literature survey. Technical Report TR 1530, Carnegie Mellon University, Department of Computer Sciences, Pittsburgh, PA, USA.

Zhu, X., and A., Goldberg. 2009. Synthesis Lectures on Artificial Intelligence and Machine Learning, Introduction to Semi-Supervised Learning. San Rafael, CA: Morgan and Claypool Publishers.Google Scholar

Zhu, X., and J., Lafferty. 2005. Harmonic mixtures: Combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. In S., Dzeroski, L. D., Raedt, and S., Wrobel, editors, Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany: ACM Press.Google Scholar