Anaphora Analysis based on ABBYY Compreno Linguistic Technologies
The paper deals with the problems of creating and tuning a system of automated anaphora resolution for Russian. Such a system is introduced, combining rule-based and machine learning approaches. It shows F-measure from 0.51 to 0.59. Freeling serves as an underlying morphological layer and an account of its quality is given, with its influence on anaphora resolution workflow. The anaphora resolution system itself is available to download and use, coming with online demo.
Статья посвящена Форуму по оценке систем автоматического распознавания анафоры и кореферентности
Many NLP researchers, especially those not working in the area of discourse processing, tend to equate coreference resolution with the sort of coreference that people did in MUC, ACE, and OntoNotes, having the impression that coreference is a well-worn task owing in part to the large number of papers reporting results on the MUC/ACE/OntoNotes corpora. Given the plethora of work on entity coreference and aware of other fora gathering coreferencerelated papers (such as LAW, DiscoMT or EVENTS), we believed that time was ripe for a new workshop on the single topic of coreference resolution that would bring together researchers who were interested in under-investigated coreference phenomena, willing to contribute both theoretical and applied computational work on coreference resolution, especially for languages other than English, less-researched forms of coreference and new applications of coreference resolution.