The paper presents several configurations of deep neural networks aimed at the task of coreference resolution for Polish. Starting with the basic feature set and standard word embedding vector size we examine the setting with larger vectors, more extensive sets of mention features, increased number of negative examples, Siamese network architecture and a global mention clustering algorithm. The highest results are achieved by the system combining our best deep neural architecture with the sieve-based approach – the cascade of rule-based coreference resolvers ordered from most to least precise. All systems are evaluated on the data of the Polish Coreference Corpus featuring 540K tokens and 180K mentions. The best variant improves the state of the art for Polish by 0.53 F1 points, reaching 81.23 points of the CoNLL metric.
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Deep Neural Networks for Coreference Resolution for Polish
Semantic Scholar · Computer Science · 2018
Abstract
The paper presents several configurations of deep neural networks aimed at the task of coreference resolution for Polish. Starting with the basic feature set and standard word embedding vector size we examine the setting with larger vectors, more extensive sets of mention features, increased number of negative examples, Siamese network architecture and a global mention clustering algorithm. The highest results are achieved by the system combining our best deep neural architecture with the sieve-based approach – the cascade of rule-based coreference resolvers ordered from most to least precise. All systems are evaluated on the data of the Polish Coreference Corpus featuring 540K tokens and 180K mentions. The best variant improves the state of the art for Polish by 0.53 F1 points, reaching 81.23 points of the CoNLL metric.