By Dan Cristea, Emanuel Dima, Corina Dima (auth.), Sobha Lalitha Devi, António Branco, Ruslan Mitkov (eds.)
This ebook constitutes the refereed court cases of the seventh Discourse Anaphora and Anaphor answer Colloquium, DAARC 2009, held in Goa, India, in November 2009.
The 10 revised complete papers offered have been rigorously reviewed and chosen from 37 preliminary submissions. The papers are prepared in topical sections on answer method, computational functions, language research, and human processing.
Read or Download Anaphora Processing and Applications: 7th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2009 Goa, India, November 5-6, 2009 Proceedings PDF
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Extra resources for Anaphora Processing and Applications: 7th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2009 Goa, India, November 5-6, 2009 Proceedings
Sample text
On the DanPASS dialogue data the algorithm that gave the best results was the weka SMO class, while for the lanchart+tv data the best results were obtained by the KStar5 class. The results of the classification algorithms in terms of Precision, Recall and F-measure are in Table 3. The table shows the baseline and the three best results obtained for each dataset by various algorithms. 5 KStar is an instance-based classifier which uses an entropy-based distance function. 22 C. Navarretta Table 3. 7 Figures 1, 2, 3 and 4 show the confusion matrices produced by the algorithms that performed best on each of the four datasets.
In: Proceedings of the 20th International Conference of Computational Linguistics, COLING 2004, Geneva, Switzerland, pp. 233–239 (2004) 22. : Annotating abstract pronominal anaphora in the DAD project. In: Proceedings of LREC 2008, Marrakesh, Morocco (May 2008) 23. : Improving machine learning approaches to coreference resolution. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002), Taipei, Taiwan, August 2002, pp. 730–736 (2002) 24. : PALinkA: a highly customizable tool for discourse annotation.
This clearly supports our stress on paying close attention to designing a strong, linguistically motivated set of features, which requires a detailed analysis of each feature individually as well as of the interaction between them. Some of the features we include, like modifiers match, are also tested by [1] and, interestingly, our ablation study comes to the same conclusion: almost all the features help, although some more than others. Hoste’s [3] work is concerned with optimization issues such as feature and sample selection, and she stresses their effect on classifier performance.