Hauptseminar
Wintersemester 2013

Integrated Models of Processing

Abstract:

In computational linguistics, processing of human language traditionally proceeds step-by-step, from identifying tokens to syntactic parsing on to semantic and functional interpretation. While conceptually simple, such a strict modularization makes it impossible to use information about the expected content given the context. It also makes it difficult to integrate knowledge about the speaker/writer into the analysis and interpretation of the language. At the same time, research in psycholinguistics and theoretical linguistics is increasingly emphasizing the need to integrate different modules of linguistic analysis.

The purpose of this seminar is to review and discuss processing approaches in computational linguistics and psycholinguistics which integrate the analysis of syntax, semantics, and context.

Instructors:

Course meets:

Language:

Credits: 10 CP in MA ISCL

Moodle page: https://moodle02.zdv.uni-tuebingen.de/course/view.php?id=529

Syllabus (this file):

Nature of course and our expectations: This Hauptseminar intends to provide an overview of the concepts and issues involved in research in this domain. Participants are expected to

  1. regularly and actively participate in class, read the papers assigned by any of the presenters and post a question on Moodle to the“Reading Discussion Forum” on each reading at the latest on the day before it is discussed in class. (20% of grade)
  2. explore and present a topic (40% of grade):
  3. write and submit a term paper (40% of grade)

Academic conduct and misconduct: Research is driven by discussion and free exchange of ideas, motivations, and perspectives. So you are encouraged to work in groups, discuss, and exchange ideas. At the same time, the foundation of the free exchange of ideas is that everyone is open about where they obtained which information. Concretely, this means you are expected to always make explicit when you’ve worked on something as a team – and keep in mind that being part of a team always means sharing the work.

For text you write, you always have to provide explicit references for any ideas or passages you reuse from somewhere else. Note that this includes text “found” on the web, where you should cite the url of the web site in case no more official publication is available.

Sessions:

References

   Altmann, G. & M. Steedman (1988). Interaction with context during human sentence processing. Cognition 30(3), 191–238.

   Boston, M. F., J. T. Hale, U. Patil, R. Kliegl & S. Vasishth (2008). Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus. Journal of Eye Movement Research 2(1), 1–12. URL http://www.jemr.org/online/2/1/1.

   Bott, O. & T. Solstad (to appear). From Verbs to Discourse: A Novel Account of Implicit Causality. In B. Hemforth, B. Mertins & C. Fabricius-Hansen (eds.), Experimental approaches to cross-linguistic meaning, Springer.

   Bransford, J. D. & M. K. Johnson (1972). Contextual Prerequisites for Understanding: Some Investigations of Comprehension and REcall. Journal of Verbal Learning and Verbal Behavior 11, 717–726.

   Demasco, P. W. & K. F. McCoy (1992). Generating text from compressed input: an intelligent interface for people with severe motor impairments. Commun. ACM 35(5), 68–78. URL http://doi.acm.org/10.1145/129875.129881.

   Demberg, V. & F. Keller (2008a). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition 109(2), 193 – 210.

   Demberg, V. & F. Keller (2008b). A psycholinguistically motivated version of TAG. In Proceedings of the 9th International Workshop on Tree Adjoining Grammars and Related Formalisms. Tübingen, pp. 25–32.

   Demberg, V., F. Keller & A. Koller (2013). Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar. Computational Linguistics URL http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00160.

   Dubey, A., F. Keller & P. Sturt (2011). A Model of Discourse Predictions in Human Sentence Processing. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Edinburgh, Scotland, UK.: Association for Computational Linguistics, pp. 304–312. URL http://www.aclweb.org/anthology/D11-1028.

   Dubey, A., F. Keller & P. Sturt (2013). Probabilistic Modeling of Discourse-Aware Sentence Processing. Topics in Cognitive Science 5, 425–451.

   Ferreira, F. & J. M. Henderson (1990). Use of verb information in syntactic parsing: Evidence from eye movements and word-by-word self-paced reading. Journal of Experimental Psychology: Learning, Memory, and Cognition 16(4), 555.

   Ferstl, E. C., A. Garnham & C. Manouilidou (2011). Implicit causality bias in English: A corpus of 300 verbs. Behavior research methods 43(1), 124–135.

   Fink, P. K. & A. W. Biermann (1986). The Correction of Ill-Formed Input using History-Based Expectation with Applications to Speech Understanding. Computational Linguistics 12(1), 13–36. URL http://aclweb.org/anthology/J86-1002.pdf.

   Fukumura, K. & R. P. van Gompel (2010). Choosing anaphoric expressions: Do people take into account likelihood of reference? Journal of Memory and Language 62(1), 52–66.

   He, Y. & S. Young (2004). Robustness Issues in a Data-Driven Spoken Language Understanding System. In S. Bangalore & H.-K. J. Kuo (eds.), HLT-NAACL 2004 Workshop: Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing. Boston, Massachusetts, USA: Association for Computational Linguistics, pp. 39–46. URL http://aclweb.org/anthology/W04-3007.pdf.

   Johnson, M. K., T. J. Doll, J. D. Bransford & R. H. Lapinsky (1972). Context Effects in Sentence Memory. Journal of Experimental Psychology 103(2), 358–360.

   Jurafsky, D. & J. H. Martin (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Upper Saddle River, NJ: Prentice Hall, 2nd edition ed. URL http://www.cs.colorado.edu/~martin/SLP/Updates/index.html.

   Kamide, Y., C. Scheepers & G. T. Altmann (2003). Integration of syntactic and semantic information in predictive processing: Cross-linguistic evidence from German and English. Journal of psycholinguistic research 32(1), 37–55.

   Kehler, A., L. Kertz, H. Rohde & J. L. Elman (2008). Coherence and coreference revisited. Journal of Semantics 25(1), 1–44.

   Köhne, J. & V. Demberg (2013a). Discourse Connectives give rise to lexical predictions. In DETEC workshop 2013. Tübingen. URL http://detec2013.files.wordpress.com/2012/12/koehne_demberg.pdf.

   Köhne, J. & V. Demberg (2013b). The time-course of processing discourse connectives. In M. Knauff, M. Pauen, N. Sebanz & I. Wachsmuth (eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (CogSci 2013, Berlin). Austin, Texas: Cognitive Science Society. URL http://www.coli.uni-saarland.de/~vera/discConn_rev.pdf.

   Koornneef, A. W. & J. J. V. Berkum (2006). On the use of verb-based implicit causality in sentence comprehension: Evidence from self-paced reading and eye tracking. Journal of Memory and Language 54(4), 445 – 465.

   Lieberman, H., A. Faaborg, W. Daher & J. Espinosa (2005). How to wreck a nice beach you sing calm incense. In Proceedings of the 10th international conference on Intelligent user interfaces. ACM, pp. 278–280. URL http://web.media.mit.edu/~lieber/Publications/Wreck-a-Nice-Beach.pdf.

   McCoy, K. F., C. A. Pennington & A. Luberoff Badman (1998). Compansion: From research prototype to practical integration. Natural Language Engineering 4, 73–95. URL http://journals.cambridge.org/article_S1351324998001843.

   Pyykkönen, P. & J. Järvikivi (2010). Activation and persistence of implicit causality information in spoken language comprehension. Experimental Psychology 57(1), 5.

   Rohde, H., R. Levy & A. Kehler (2011). Anticipating explanations in relative clause processing. Cognition 118(3), 339–358.

   Sayeed, A. & V. Demberg (2013). The semantic augmentation of a psycholinguistically-motivated syntactic formalism. In Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL). Sofia, Bulgaria: Association for Computational Linguistics, pp. 57–65. URL http://www.aclweb.org/anthology/W13-2607.

   Swets, B., T. Desmet, C. Clifton & F. Ferreira (2008). Underspecification of syntactic ambiguities: Evidence from self-paced reading. Memory & Cognition 36(1), 201–216.

   Van Berkum, J. J. A., C. M. Brown, P. Zwitserlood, V. Kooijman & P. Hagoort (2005). Anticipating Upcoming Words in Discourse: Evidence From ERPs and Reading Times. Journal of Experimental Psychology: Learning, Memory, and Cognition 31(3), 443–467.

   Van Gompel, R. P. G. & M. J. Pickering (2007). Syntactic parsing. In The Oxford handbook of psycholinguistics, Oxford University Press, chap. 17, pp. 289–307.

   von der Malsburg, T. & S. Vasishth (2012). Scanpaths reveal syntactic underspecification and reanalysis strategies. Language and Cognitive Processes 0(0), 1–34. URL http://www.tandfonline.com/doi/abs/10.1080/01690965.2012.728232.

   Wandmacher, T. (2008). Adaptive word prediction and its application in an assistive communication system. Ph.D. thesis, Universität Tübingen & Université Fraçois-Rabelais de Tours.

Last update: January 17, 2014