Information Extraction Based on Deep Syntactic-Semantic Analysis
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.