By Iris Hendrickx, Orphée De Clercq, Veronique Hoste (auth.), Iris Hendrickx, Sobha Lalitha Devi, António Branco, Ruslan Mitkov (eds.)
This booklet constitutes the refereed complaints 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 structures; language research and illustration; and human processing and performance.
Read Online or Download Anaphora Processing and Applications: 8th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2011, Faro, Portugal, October 6-7, 2011. Revised Selected Papers PDF
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Additional info for Anaphora Processing and Applications: 8th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2011, Faro, Portugal, October 6-7, 2011. Revised Selected Papers
We look up the encoded token keys in our big hash of encoded values, and then use the VL to decode the retrieved values back to pairs of counts, which of course provide the feature values in our classiﬁer. However, this is still wasteful, since one can observe from the sample above that most tokens in the N-grams don’t change from line-to-line. So, to make the data even smaller in our ﬁnal ﬁle on disk (which makes it faster to load from disk into memory) for each N-gram we only store the changes from the previous N-gram (having special ﬂags to indicate when changes happen and which tokens have changed).
We conﬁrmed the worse performance of classiﬁers in the task of coreference resolution. ME ranker turned out to be a universal solution, providing the best F-measure as well as precision score. Nevertheless, its performance does not seem to achieve a success rate high enough to help in a practical usage. In spite of that we plan to incorporate it into TectoMT and evaluate this CR system as a component of the end-to-end Natural Language Processing pipeline. A crucial ﬁnding comes from the analysis of the learning curves.
7 million English sentences in Europarl  (a speed of approximately 12 thousand sentences per second). 5 seconds, at a speed of roughly 20 thousand sentences per second. Of course, many of these sentences are passed unmodiﬁed if they do not contain an instance of it; on the 6511 WSJ sentences containing an it, Nada tags around 3900 sentences per second. We then assess the value of the diﬀerent features, alone and in combination, on the three test sets (Table 2). The system with Lexical features can be regarded as a reasonable approximation of a range of previous supervised approaches [12,4,25] (but trained and evaluated on more data), while the Web Counts system is a reasonable (but practical) approximation of the Bergsma et al.
Anaphora Processing and Applications: 8th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2011, Faro, Portugal, October 6-7, 2011. Revised Selected Papers by Iris Hendrickx, Orphée De Clercq, Veronique Hoste (auth.), Iris Hendrickx, Sobha Lalitha Devi, António Branco, Ruslan Mitkov (eds.)