Computing Point-of-View: Modeling and Simulating Judgments of Taste
|
|  Yüklə 8.55 Mb.
|
|
Riloff, E., & J.M. Wiebe: 2003, Learning extraction patterns for subjective expressions, Proceedings of EMNLP-03, 8th Conference on Empirical Methods in Natural Language Processing: 105-112. Roget, P.: 1911, Roget’s Thesaurus of English Words and Phrases. Retrieved from gutenberg.net/etext/10681 Rokeby, D.: 1995, Transforming Mirrors: Subjectivity and Control in Interactive Media, in Penny, S. (Ed.), Critical Issues in Electronic Media: 133-158, Series in Series in Film History and Theory, Albany: SUNY Press. Rosenblatt, L.: 1978, Efferent and Aesthetic Reading, The Reader, The Text, The Poem: A Transactional Theory of the Literary Work: 22-47, Carbondale: Southern Illinois UP. Rubin, V.L., J.M. Stanton & E.D. Liddy: 2004, Discerning Emotions in Texts, The AAAI Symposium on Exploring Attitude and Affect in Text, AAAI-EAAT 2004, Stanford, CA. Sack, W.: 1994, On the Computation of Point of View, Proceedings of the National Conference of Artificial Intelligence (AAAI 94), Seattle, WA., July 31-August 4. Sack, W.: 2001, Actor-Role Analysis: Ideology, Point of View and the News, New Perspectives on Narrative Perspective (Will Van Peer and Seymour Chatman, Editors) New York: SUNY Press. Salton, G. & C. Buckley: 1988, Term weighting approaches in automatic text retrieval, Information Processing and Management, 24(5): 513-- 523. Salton, G. & M.J. McGill: 1983, Introduction to Modern Retrieval, McGraw-Hill Book Company. Sarwar, B.M., G. Karypis, J.A. Konstan, J.T. Riedl: 2001, Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference, 285--295. Schapire, R.E. & Y. Singer: 2000, BoosTexter: A boosting-based system for text categorization, Machine Learning 39(2/3), 135-168. Springer. Schleiermacher, F.: 1809/1998, General Hermeneutics, in A. Bowie (Ed.) Schleiermacher: Hermeneutics and Criticism: 227-268. Cambridge University Press. Selker, T.: 1994, COACH: a teaching agent that learns, Communications of the ACM 37(7): 92-99. Shardanand, U. and P. Maes: 1995, Social information filtering: Algorithms for automating `word of mouth'. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems: 210-217. Silber, G., K.F. McCoy: 2002, Efficiently computed lexical chains as an intermediate representation for automatic text summarization, Computational Linguistics 28(4): 487-496. Silverman, K.: 1983, The subject in semiotics, Oxford University Press. Simmel, G.: 1971, D.N. Levine (Ed.): On Individuality and Social Forms: Selected Writings of Georg Simmel, University of Chicago Press. Simon, H.: 1967, Motivational and emotional controls of cognition, Psychological Review, 74(1), 29-39. Singh, P., T. Lin, E. T. Mueller, G. Lim, T. Perkins, W. L. Zhu: 2002, Open Mind Common Sense: knowledge acquisition from the general public. Proceedings of ODBASE’2002. Sloman, A. & M. Croucher: 1981, Why Robots Will Have Emotions, Proceedings of the Seventh International Conference on Artificial Intelligence. Solomon, M.R., H. Assael: 1987, The forest or the trees – a gestalt approach to symbolic consumption, in Umiker-Sebeok (Eds),Marketing and Semiotics – New Directions in the Study of Signs for Sale, 189-218, Mouton-Gruyter. Subasic, P. & A. Huettner: 2001, Affect analysis of text using fuzzy semantic typing, IEEE Transaction on Fuzzy Systems 9(4): 483-496. Staab, S., S. Santini, F. Nack, L. Steels, and A. Maedche: 2002, Emergent semantics, IEEE Intelligent Systems, 17(1):78--86. St. John, M.F.: 1992, The story gestalt: A model of knowledge-intensive processes in text comprehension, Cognitive Science, 16, 271-306. Talmy, L.: 1988a, Force Dynamics in language and cognition, Cognitive Science, 12(1), 49-100. Tan, E.S.: 1994, Story processing as an emotion episode. Naturalistic Text Comprehension, H. van. Oostendorp and RA Zwaan (eds.), 165–188 Todorov, T.: 1968/1981, Introduction to Poetics, Theory and History of Literature Vol. 1, University of Minnesota Press. Tulving, E.: 1983, Elements of episodic memory. Oxford: New York Turney, P.D.: 2001, Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL, Proceedings of the Twelfth European Conference on Machine Learning. Turney, P.: 2002, Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews, Proceedings of ACL-02, 40th Annual Meeting of the Association for Computational Linguistics: 417-424, Association for Computational Linguistics. Turney, P.D. & M.L. Littman: 2003, Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4): 315-346. Tversky, A.: 1977, Features of similarity, Psychological Review 84:327--352. Wahlster, W.: 1991, User and Discourse Models for Multimodal Communication, J. Sullivan and S. Tyler (eds.), Intelligent User Interfaces: 45-67, ACM Press. Watson, D., A. Tellegen: 1985, Toward a consensual model of mood, Psychol Bull 98(2): 219-35 Whissell, C.: 1989, The Dictionary of Affect in Language. In R. Plutchnik and H. Kellerman, editors, Emotion: Theory, Research and Experience, 113--131. NY: Academic Press. Whitman, B. & S. Lawrence: 2002, Inferring Descriptions and Similarity for Music from Community Metadata, Voices of Nature, Proceedings of the 2002 International Computer Music Conference: 591-598. Whitman, B. & P. Smaragdis: 2002, Combining Musical and Cultural Features for Intelligent Style Detection, Proceedings of the 3rd International Conference on Music Information Retrieval. Wiebe, J., R.F. Bruce & T.P. O'Hara: 1999, Development and use of a gold-standard data set for subjectivity classifications, Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics: 246-253, College Park,US: Association for Computational Linguistics. Wiebe, J.M. & W.J. Rapaport: 1988, A computational theory of perspective and reference in narrative, Proc. 26th Annual Meeting of the Assoc. for Computational Linguistics (ACL-88): 131-138. Wiebe, J., T. Wilson, R. Bruce, M. Bell, M. Martin: 2004, Learning Subjective Language, Computational Linguistics 30(3), 207-308. Wilensky, R.: 1978, Understanding goal-based stories (Technical Report YALE/DCS/tr140), New Haven, CT: Computer Science Department, Yale University. Wilson, T., J. Wiebe & P. Hoffman: 2005, Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis, Proceedings of HLT/EMNLP 2005 Winograd, T.: 1971, Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. MIT AI Technical Report 235. Yarowsky, D.: 1992, Word-sense disambiguation using statistical models of Roget's categories trained on large corpora, Proc. of the 33th Annual Meeting of the Association for Computational Linguistics: 189--196. Zholkovsky, A.K.: 1970b: Porozhdayuschaya poetika v rabotakh S. M. Eizenshteina (Generative poetics in the writings of Eisenstein), in Greimas (ed.) Sign-Language-Culture: 451-68, The Hague, Mouton. Zukerman, I. & D.J. Litman: 2001, Natural Language Processing and User Modeling: Synergies and Limitations, User Modeling and User-Adapted Interaction 11: 49-63, Kluwer Academic Publisher. Zwaan, R.A. and G.A. Radvansky: 1998, Situation models in language comprehension and memory. Psychological Bulletin, 123(2): 162-185 1 The literature review in the thesis summary is merely indicative. A more complete survey of related work is achieved in later chapters—for user modeling (Section 2.1), interactive user interfaces (Section 2.5), computational reading (Section 3.1), text mining and social network analysis (Section 3.2), commonsense reasoning (Section 3.3), and textual affect analysis (Section 3.4). 2 http://myspace.com 3 http://friendster.com 4 http://orkut.com 5 http://facebook.com 6 http://www.dmoz.org 7 http://wikipedia.org 8 It should be noted that discussions of particularly substantial techniques and technologies have been lifted out of this chapter and moved to Chapter 3 in order to improve the flow of discussion. 9 http://amazon.com 10 A fourth, “zeroth” step is deciding on sources of personal texts that are a good source of the information for person models in each realm. 11 though admittedly, it is in essence a memory-based representation. Organizing memories into sheets here visually affords metaphors like ‘attitude alignment’ 12 Jung means his key words ‘think’, ‘feel’, ‘intuit’, and ‘sensing’ differently from their common sense interpretations. 13 14 Since RATE readers need a large corpus of texts to gist from, we assumed that a person’s corpus of texts that often spanned months or years would still contain time-stable attitudes. The phenomenon of attitudes changing over time was not dealt with in this thesis. 15 Significant identities are called classemes, whereas significant interests are called semes. This was a deliberate semantical choice. A classeme is a particularly dominant seme that is also more abstract and contextual than a seme. Identities are extracted inexactly from the text, hence classemes; whereas interests are explicit in the text, hence semes. 17 a qualifying clique edge is defined here as an edge whose strength is in the 80th percentile, or greater, of all edges 18 by discounting a random 50% subset of the clique’s edges by a Gaussian factor (0.5 mu, 0.2 sigma). 19 http://bloginality.love-productions.com/ 20 It is suggested that syntagmatic expression is the aesthetic mode of our present social and cultural reality. The validity of the present viewpoint computation is seated in this presupposition. However, this post-structural aesthetics is not more ‘final’ or closer to ‘truth’ than aesthetics of antiquity or of the Enlightenment. Nietzsche called our times the ‘age of comparison’, yet he presaged “a posterity that knows it has transcended both the completed original folk cultures, as well as the culture of comparison, but that looks back on both kinds of culture as on venerable antiquities, with gratitude.” [HumanAllTooHuman, p.30, aph 23, transl: marion faber w/ Stephen lehmann] 21 Hubs are known as ‘attractors’ in the dynamical sociological systems literature. 22 http://en.wikipedia.org/wiki/List_of_subcultures 23 being careful to not confuse the actant in Latour versus in Greimas, which are slightly different notions. |