8. An apparatus comprising: at least one processor and a computer readable storage medium having program code stored thereon for identifying the focus of a document, in a natural language processing application, the natural language processing application comprising a hierarchical concept tree having a plurality of nodes, each node being associated with a term, by executing the following steps: mapping an input document to nodes in a concept tree to determine a number of occurrences of a term in the input document which also occur at each of the nodes in the concept tree, weighting each node in the concept tree, depending on the determined number of occurrences of the term in the input document and a determined value assigned to each node in the concept tree, wherein the determined value assigned to each node in the concept tree comprises a probability that, for a given one of the occurrences of the term in the input document, the node is a correct one of a plurality of nodes within the concept tree that are associated with the term, wherein the probability is based on a total number of the plurality of nodes within the concept tree that are associated with the term, and wherein the weighting of each node in the concept tree comprises multiplying the assigned determined value and the determined number of occurrences of the associated term in the input document, traversing the concept tree to identify a heaviest weighted path, in dependence of the weighting of each node in the concept tree, and determining the focus of the input document by identifying a node having the heaviest weight along the most heavily weighted path.
8. An apparatus comprising: at least one processor and a computer readable storage medium having program code stored thereon for identifying the focus of a document, in a natural language processing application, the natural language processing application comprising a hierarchical concept tree having a plurality of nodes, each node being associated with a term, by executing the following steps: mapping an input document to nodes in a concept tree to determine a number of occurrences of a term in the input document which also occur at each of the nodes in the concept tree, weighting each node in the concept tree, depending on the determined number of occurrences of the term in the input document and a determined value assigned to each node in the concept tree, wherein the determined value assigned to each node in the concept tree comprises a probability that, for a given one of the occurrences of the term in the input document, the node is a correct one of a plurality of nodes within the concept tree that are associated with the term, wherein the probability is based on a total number of the plurality of nodes within the concept tree that are associated with the term, and wherein the weighting of each node in the concept tree comprises multiplying the assigned determined value and the determined number of occurrences of the associated term in the input document, traversing the concept tree to identify a heaviest weighted path, in dependence of the weighting of each node in the concept tree, and determining the focus of the input document by identifying a node having the heaviest weight along the most heavily weighted path. 9. The apparatus as claimed in claim 8 , the steps further comprising determining if the determined focus is too specific for the input document and on a positive determination identifying a specific focus of the input document by identifying a node having a value which is less than the sum of the node's child nodes.