By Iris Hendrickx, Orphée De Clercq, Veronique Hoste (auth.), Iris Hendrickx, Sobha Lalitha Devi, António Branco, Ruslan Mitkov (eds.)
This publication constitutes the refereed lawsuits of the eighth Discourse Anaphora and Anaphor solution Colloquium, DAARC 2011, held in Faro, Portugal, in October 2011. The sixteen revised complete papers offered have been rigorously reviewed and chosen from forty five preliminary submissions. The papers are prepared in topical sections on computational solution technique and platforms; language research and illustration; and human processing and performance.
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Additional resources for Anaphora Processing and Applications: 8th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2011, Faro, Portugal, October 6-7, 2011. Revised Selected Papers
A crucial ﬁnding comes from the analysis of the learning curves. The combination of presented feature sets and learning methods are not able to suﬃciently exploit a growing amount of knowledge in a growing quantity of the annotated data. We took advantage of the tree nature of the annotated data to design several novel features. Most of them aﬀect the quality of resolution positively, though there is still a place for smarter involvement of dependent nodes of phrases. Our future work should focus on feature engineering, mainly to investigate deeper lexical and semantic relations between phrases.
There is no signiﬁcant diﬀerence between F-values of the two ranking approaches. However, if the CR system is to be used as an aid for annotators, high values of precision are preferred. From this point of view, maximum entropy ranker performs better than perceptron ranker. Except for the ﬁnal evaluation we were interested how models deal with quantitative and qualitative changes. Since annotation of the data we exploited is not ﬁnished, ﬁndings on the former can give us information, whether it is worth going on in annotation process.
In recent years unsupervised approaches made signiﬁcant performance progress as well. Indeed, Haghighi and Klein  created an unsupervised system based on inference in Bayesian networks, which outperforms best known supervised resolvers. 4 Extracted Features Features the resolver works with can be divided into following categories: Grammatical: These features are extracted from m-layer and consist of morphological tags of the anaphor and the antecedent, agreement in number, gender and negation.