10. A system for automatically expanding existing data content that is included in a corpus comprising: memory storage device; a processor in communication with said memory storage device configured to: automatically identify a topic from existing data in said corpus, said existing data comprising one or more seed documents; automatically generate search queries to search for content related to said topic identified from said existing data, the queries being generated based on said topic identified from a seed document in said corpus; using said generated search queries to automatically conduct a search in and retrieve content from one or more other data repositories not including said corpus; automatically extract units of text from the retrieved content; automatically determine a relevance of the extracted units of text and their relatedness to the topic identified from the existing data; and automatically select new sources of content and include them in the corpus based on the determined relevance to said identified topic including compiling a new document from the most relevant extracted text units, said new document being searchable with said existing data content, wherein to automatically identify said topic, said processor is further configured to: generate from said one or more seed documents, a topic name and a topic descriptor corresponding to units extracted from said one or more documents, said generated search queries including one or more: said topic name or words and phrases extracted from said topic descriptor, and wherein to retrieve content, said processor is further configured to: use search engines to run said search queries against the one or more external data repositories, said content retrieved including one or more text passages or documents; extract units of text by splitting the retrieved text passages or documents into smaller text units using structural markup for demarcating text unit boundaries; and, said processor further configured to: determine the relevance of the text units from said retrieved passages or documents by scoring each said text unit using a statistical model based on a lexico-syntactic feature, said lexico-syntactic feature includes a topicality feature, a search feature and a surface feature; wherein to automatically determine a relevance of the extracted units includes fitting a logistic regression (LR) model using said topicality, search and surface features and a generation level to estimate a relevance score of each independent text unit based on their relevance to said topic of the seed document; and compute a score based further on a topicality feature by one of: computing a likelihood ratio of a text unit estimated with a topic model and a background language model, said topic model being estimated from the seed document, and said background language model being estimated from a sample of documents from said corpus.
10. A system for automatically expanding existing data content that is included in a corpus comprising: memory storage device; a processor in communication with said memory storage device configured to: automatically identify a topic from existing data in said corpus, said existing data comprising one or more seed documents; automatically generate search queries to search for content related to said topic identified from said existing data, the queries being generated based on said topic identified from a seed document in said corpus; using said generated search queries to automatically conduct a search in and retrieve content from one or more other data repositories not including said corpus; automatically extract units of text from the retrieved content; automatically determine a relevance of the extracted units of text and their relatedness to the topic identified from the existing data; and automatically select new sources of content and include them in the corpus based on the determined relevance to said identified topic including compiling a new document from the most relevant extracted text units, said new document being searchable with said existing data content, wherein to automatically identify said topic, said processor is further configured to: generate from said one or more seed documents, a topic name and a topic descriptor corresponding to units extracted from said one or more documents, said generated search queries including one or more: said topic name or words and phrases extracted from said topic descriptor, and wherein to retrieve content, said processor is further configured to: use search engines to run said search queries against the one or more external data repositories, said content retrieved including one or more text passages or documents; extract units of text by splitting the retrieved text passages or documents into smaller text units using structural markup for demarcating text unit boundaries; and, said processor further configured to: determine the relevance of the text units from said retrieved passages or documents by scoring each said text unit using a statistical model based on a lexico-syntactic feature, said lexico-syntactic feature includes a topicality feature, a search feature and a surface feature; wherein to automatically determine a relevance of the extracted units includes fitting a logistic regression (LR) model using said topicality, search and surface features and a generation level to estimate a relevance score of each independent text unit based on their relevance to said topic of the seed document; and compute a score based further on a topicality feature by one of: computing a likelihood ratio of a text unit estimated with a topic model and a background language model, said topic model being estimated from the seed document, and said background language model being estimated from a sample of documents from said corpus. 17. The system as claimed in claim 10 , wherein each said topicality lexico-syntactic feature is specified as one of: a continuous, binary or discrete type feature, and as being generated at one of: a document level or text unit level, said fitting a logistic regression (LR) model using said topicality, search and surface features at one of said continuous, binary or discrete types and a generation level.