Сравнения двух методов автоматического извлечения участников события из неструктурированных источников
This paper proposes the model that is used to describe relations between verb and set expressions during analysis of the sentence in the Russian language. It describes the developed knowledge base that is used to describe syntactic units of the Russian language. The model is used to objects identification and knowledge extraction from the text in Russian language.
This paper presents a rule-based approach to Information Extraction (IE) task within FactRuEval-2016 competition. Our system is based on ABBYY Compreno Technology. The technology uses the results of deep syntactic-semantic analysis, which leads to significant reduction of the number of necessary rules and makes them laconic. The evaluation was conducted on FactRuEval dataset. FactRuEval is an open evaluation of IE systems. The participants could take part in three tracks. The first track required to detect the boundaries and type of named entities in a text. The second track required to extract normalized attributes and perform local identification of named entities. The third track required to extract facts of certain types from a text. We took part in all three of the tracks with the nickname violet. Our method proved to be successful: we have achieved high F-measures in Named Entity Recognition tracks and the highest F-measure in Fact Extraction track.
In this paper we consider choice problems under the assumption that the preferences of the decision maker are expressed in the form of a parametric partial weak order without assuming the existence of any value function. We investigate both the sensitivity (stability) of each non-dominated solution with respect to the changes of parameters of this order, and the sensitivity of the set of non-dominated solutions as a whole to similar changes. We show that this type of sensitivity analysis can be performed by employing techniques of linear programming.