A computer system for extraction of data and related information from tables in electronic documents having at least one processor and at least one memory, the system comprising: means for automatically identifying and tagging a text segment in an electronic document; means for automatically tagging entity names, monetary expressions, and temporal expressions within the text segment; means for identifying a financial event described within the automatically tagged text segment; a support vector machine classifier adapted to screen the document and identify a table comprising information of interest by distinguishing tables from non-tables and wherein tables used for formatting reasons are identified as non-tables, the information of interest comprising a plurality of desired attributes and desired values, identified genuine tables are processed by: a. table classification using relation-specific classifiers based on supervised machine learning, b. label row and column classification by distinguishing between label column and label rows from values inside the tables, c. table structure recognition by associating each value with their labels in the same column and the same row to generate a list of attribute-value pairs, d. table understanding by comparing each of the attribute-value pairs; means for defining in memory a data record associated with the financial event, the data record including data derived from the tagged text segment; and means for extracting relationship data from the text segment and for determining a role of at least one entity, the entity being tagged within the text segment and related to the data record. means for automatically identifying and tagging a text segment in an electronic document; means for automatically tagging entity names, monetary expressions, and temporal expressions within the text segment; means for identifying a financial event described within the automatically tagged text segment; a support vector machine classifier adapted to screen the document and identify a table comprising information of interest by distinguishing tables from non-tables and wherein tables used for formatting reasons are identified as non-tables, the information of interest comprising a plurality of desired attributes and desired values, identified genuine tables are processed by: a. table classification using relation-specific classifiers based on supervised machine learning, b. label row and column classification by distinguishing between label column and label rows from values inside the tables, c. table structure recognition by associating each value with their labels in the same column and the same row to generate a list of attribute-value pairs, d. table understanding by comparing each of the attribute-value pairs; a. table classification using relation-specific classifiers based on supervised machine learning, b. label row and column classification by distinguishing between label column and label rows from values inside the tables, c. table structure recognition by associating each value with their labels in the same column and the same row to generate a list of attribute-value pairs, d. table understanding by comparing each of the attribute-value pairs; means for defining in memory a data record associated with the financial event, the data record including data derived from the tagged text segment; and means for extracting relationship data from the text segment and for determining a role of at least one entity, the entity being tagged within the text segment and related to the data record.