Recent developments in computational discourse processing (Seminar)

References

A very useful introductory textbook: M. Stede. Discourse Processing. Morgan & Claypool, 2011.
More background in: Chapter 21, D. Jurafsky and J. Martin. Speech and Language Processing. Pearson, 2009.

Below, you can find a list of topics/session. For each topic, you should present the paper(s) listed for this topic. If there are multiple papers, you are expected to present one (sometimes two) of them in detail (title(s) in red), and give an overview of what's happening in the other(s). In some cases additional references are provided for background information. Ask us if you have any questions!

1. Introduction to Computational Discourse
   • Bonnie Webber, Markus Egg, Valia Kordoni, 2012. Discourse structure and language technology. Natural Language Engineering, 18.
2. Text Tiling - Tristan
   • Marti Hearst: TextTiling: Segmenting Text into Multi-paragraph Subtopic Passages, Computational Linguistics, 23(1), 1997.
   • Jane Morris and Graeme Hirst: Lexical Cohesion Computed by Thesaural Relations as an Indicator of the Structure of Text. Computational Linguistics 17(1), pp. 21-48, 1991.
   • (additional background) Graeme Hirst and David St-Onge: Lexical chains as representations of context for the detection and correction of malapropisms. in: Christiane Fellbaum (ed.), WordNet: An electronic lexical database, pp. 305--332, Cambridge, MA:, The MIT Press, 1998.
3. Document Compression - Patrick
   • James Clarke and Mirella Lapata, 2010: Discourse Constraints for Document Compression. Computational Linguistics 36(3).
   • (background on Centering Theory) M. Poesio, H. Cheng, R. Henschel, J. Hitzeman, R. Kibble, R. Stevenson, 2000. Specifying the parameters of centering theory: a corpus-based evaluation using text from application-oriented domainsProceedings of ACL 2000.
   • (more on Centering Theory) Nikiforos Karamanis, Chris Mellish, Massimo Poesio, Jon Oberlander, 2009: Evaluating centering for information ordering using corpora. Computational Linguistics 35(1).
4. Penn Discourse Treebank & Parser (PDTB) - Nikos
   • Lin, Ziheng, Hwee Tou Ng, and Min-Yen Kan. A PDTB-styled end-to-end discourse parser. Natural Language Engineering (2012): 1-34. (FOCUS ON THIS PAPER)
   • The PDTB Research Group. The Penn Discourse TreeBank 1.0. Annotation Manual. IRCS Technical Report IRCS-06-01, Institute for Research in Cognitive Science, University of Pennsylvania. March 2006. (no need to read all of this, but some sections may be useful.)
   • Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi and Bonnie Webber.The Penn Discourse TreeBank. In Proceedings of the Language Resources and Evaluation Conference. Lisbon, Portugal. 2004.
5. Rhetorical Structure Theory & Parser (RST)
   • Mann, W.C., & Thompson, S.A. 1988. Text, 8 (3). 243-281. A useful overview is here.
   • Soricut, Radu, and Daniel Marcu. Sentence Level Discourse Parsing using Syntactic and Lexical Information Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, 2003.
6. Entity grid/discourse role matrix for coherence modeling - Richard
   • Barzilay, R., & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, (May 2007).
     OR (shorter, but also a little hard to read): Barzilay, R., & Lapata, M. (2008). Modeling local coherence: An entity-based approach. ACL 2005.
   • Lin, Z., Ng, H. T., & Kan, M. (2011). Automatically Evaluating Text Coherence Using Discourse Relations. ACL-HLT.
7. Topic Modeling - Bryan (Please make sure to explain some background about topic models)
   
   • Barzilay, R., & Lee, L. (2004).Catching the drift: Probabilistic content models, with applications to generation and summarization. Proceedings of HLT-NAACL.
   • Chen, H., Branavan, S. R. K., Barzilay, R., & Karger, D. R. (2009). Global Models of Document Structure Using Latent Permutations. HLT-NAACL 2009 (pp. 371379).
   • Lapata, Mirella. Probabilistic text structuring: Experiments with sentence ordering. Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003.
8. Discourse Relations vs. Entity-based Coherence - Zhe
   • Louis, A., & Nenkova, A. (2010). Creating Local Coherence: An Empirical Assessment. NAACL-HLT.
   • Louis, A., Joshi, A., Prasad, R., Nekova, Ani (2010): Using Entity Features to Classify Implicit Discourse Relations. SigDial 2010.
9. Temporal Relation Processing - Susanne
   • TempEval-2014 overview: UzZaman et al. (2013): SemEval-2013 Task 1: TempEval-3
   • Steven Bethard and James H Martin. 2006. Identification of event mentions and their semantic class. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
   • Franco Costa and Antonio Branco. 2014. Aspectual Type and Temporal Relation Classification. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 266–275, Avignon, France, April 23 - 27 2012.
   • Steven Bethard. 2013. ClearTK-TimeML: A minimalist approach to TempEval 2013. In Second Joint Conference on Lexical and Computational Semantics (* SEM), volume 2, pages 10–14.
10. Situation Entity Classification
    • Carlota Smith: Aspectual Entities and tense in discourse. In press P. Kempchimsky & S. Roumyana (eds), The Syntax, Semantics and Acquisition of Aspect.. Kluwer. (2005).
    • Palmer, Alexis, et al.: A sequencing model for situation entity classification ACL 2007. (FOCUS ON THIS PAPER)
11. Coreference Resolution - Omid
    • Raghunathan et al: A multi-pass sieve for coreference resolution. EMNLP 2010.
    • Jie Cai and Michael Strube: End-to-End coreference resolution via hypergraph partitioning. Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, 2010.
12. Narrative Chains - Liesa
    • Chambers & Jurafsky: Unsupervised Learning of Narrative Event Chains. ACL 2008.
    • Chambers & Jurafsky: Unsupervised Learning of Narrative Schemas and their Participants. ACL 2009.
13. Discourse in Machine Translation - Kleo
    • Thomas Meyer; Bonnie Webber: Implicitation of Discourse Connectives in (Machine) Translation. In Proceedings of the 1st DiscoMT Workshop at ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics).
    • Thomas Meyer, Lucia Polakova: Machine Translation with Many Manually Labeled Discourse Connectives, In Proceedings of the 1st DiscoMT Workshop at ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics).
14. Sentiment analysis - Ilya
    • Liu, Bing (2010) Sentiment analysis and subjectivity. In N. Indurkhya and D. F. J. (Eds.), Handbook of Natural Language Processing. Boca Raton, Fl: CRC Press.
    • Heerschop, Bas, Frank Goossen, Alexander Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong (2011) Polarity Analysis of Texts using Discourse Structure. CIKM 2011.
    • Taboada, Maite, Julian Brooke, Milan Tofiloski, Kimberly Voll and Manfred Stede (2011) Lexicon-based methods for sentiment analysis. Computational Linguistics, 37 (2): 267-307.
15. Summarization - Madhumita
    • Janara Christensen, Mausam, Stephen Soderland, Oren Etzioni, 2013. Towards Coherent Multi-Document Summarization, Proceedings of NAACL-HLT 2013.
    • Ani Nenkova and Kathleen McKeown, 2011. Automatic Summarization. Foundations and Trends in Information Retrieval 5(2-3).
16. Essay scoring - Anjana
    • Yigal Attali and Jill Burstein. 2006. Automated essay scoring with e-rater(R) V.2, Journal of Technology, Learning and Assessment, Volume 4, Issue 3.
    • Jill Burstein, Joel Tetrault, and Martin Chodorow. 2013. Holistic Discourse Coherence Annotation for Noisy Essay Writing, Dialogue \& Discourse, Volume 4, Issue 2.
    • Beata Beigman Klebanov, Nitin Madnani, Jill Burstein, and Swapna Somasundaran. 2014. Content Importance Models for Scoring Writing From Sources. ACL 2014, to appear.
17. Discourse relation classification - Clayton
    • Caroline Sporleder and Alex Lascarides, 2008. Using Automatically Labelled Examples to Classify Rhetorical Relations: An Assessment, Natural Language Engineering, Volume 14, Issue 03, July 2008.
    • Fatemeh Torabi Asr and Vera Demberg, 2012. Implicitness of Discourse Relations. In Proceedings of COLING 2012, Mumbai, India, Dec 2012.
18. Assessing text quality
    • Annie Louis and Ani Nenkova. 2013. What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain. Transactions of TACL, 2013.
    • Ani Nenkova, Jieun Chae, Annie Louis, Emily Pitler. 2010. Structural features for predicting the linguistic quality of text. Empirical methods in natural language generation. 2010.