7. A system for searching an electronic document for passages relating to a concept being searched for, where the concept is expressed as a word or plurality of words, the system comprising: a computer processor deconstructing training electronic texts stored on a computer readable into a stream of features; a data store storing the stream of features; wherein the features include the text of complete sentences, tokens used by the text in each sentence, the sequence of sentences, layout of text and typography of text; wherein the computer processor executes a conditional random field algorithm to label sentences in the electronic document as either being relevant to the concept being searched for (“State A”) or as background information (“State B”) based on the stream of features; and wherein the computer processor executes a search algorithm which returns those sentences labelled as State A; wherein the conditional random field algorithm generates a probability of a sentence being relevant to State A; wherein the probability includes a tolerance for words or portions of words which cannot be resolved into computer-readable text; wherein, given a document containing multiple sentences S:={s 1 , s 2 , . . . , s m } and the corresponding concept label for each sentence Concept:={concept 1 , concept 2 , . . . , concept m }, the conditional random field function defining the probability of the Concept applied to S, Pr(Concept|S), is expressed as: Pr ⁡ ( Concept ❘ S ) = 1 Z s ⁢ exp ⁡ ( ∑ j = 1 K × L ⁢ ⁢ F j ⁡ ( Concept , S ) ) = 1 Z s ⁢ exp ⁡ ( ∑ i = 1 , k = 1 m , K ⁢ ⁢ λ k ⁢ f k ⁡ ( y i - 1 , ⁢ y i , S ) + ∑ i = 1 , l = 1 m , L ⁢ ⁢ μ l ⁢ g l ⁡ ( y i , S ) ) where Z s is a normalization constant, ƒ k (y i-1 ,y i ,S) is an arbitrary feature function over the group of sentences in a document and positions i and i−1, g l (y i ,S) is a feature function of the state at position i and the document S.
7. A system for searching an electronic document for passages relating to a concept being searched for, where the concept is expressed as a word or plurality of words, the system comprising: a computer processor deconstructing training electronic texts stored on a computer readable into a stream of features; a data store storing the stream of features; wherein the features include the text of complete sentences, tokens used by the text in each sentence, the sequence of sentences, layout of text and typography of text; wherein the computer processor executes a conditional random field algorithm to label sentences in the electronic document as either being relevant to the concept being searched for (“State A”) or as background information (“State B”) based on the stream of features; and wherein the computer processor executes a search algorithm which returns those sentences labelled as State A; wherein the conditional random field algorithm generates a probability of a sentence being relevant to State A; wherein the probability includes a tolerance for words or portions of words which cannot be resolved into computer-readable text; wherein, given a document containing multiple sentences S:={s 1 , s 2 , . . . , s m } and the corresponding concept label for each sentence Concept:={concept 1 , concept 2 , . . . , concept m }, the conditional random field function defining the probability of the Concept applied to S, Pr(Concept|S), is expressed as: Pr ⁡ ( Concept ❘ S ) = 1 Z s ⁢ exp ⁡ ( ∑ j = 1 K × L ⁢ ⁢ F j ⁡ ( Concept , S ) ) = 1 Z s ⁢ exp ⁡ ( ∑ i = 1 , k = 1 m , K ⁢ ⁢ λ k ⁢ f k ⁡ ( y i - 1 , ⁢ y i , S ) + ∑ i = 1 , l = 1 m , L ⁢ ⁢ μ l ⁢ g l ⁡ ( y i , S ) ) where Z s is a normalization constant, ƒ k (y i-1 ,y i ,S) is an arbitrary feature function over the group of sentences in a document and positions i and i−1, g l (y i ,S) is a feature function of the state at position i and the document S. 11. The system according to claim 7 , wherein each feature in the stream of features is tagged using natural language processing techniques.