10. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for estimating a similarity level between documents, the method comprising: extracting, from a set of documents, a set of semantic entities, wherein a respective semantic entity includes a meaningful sequence of characters; determining, for the respective semantic entity, a predefined word group to which the respective semantic entity belongs, wherein the predefined word group indicates a content category associated with the respective semantic entity; computing an inverse document frequency (IDF) value for the respective semantic entity; assigning a weight to the computed IDF value based on the predefined word group to which the respective semantic entity belongs; calculating the similarity level sim(A,B) between a respective document A in the set of documents and a target document B, based on weighted IDF values associated with one or more of the extracted semantic entities, wherein calculating the similarity level involves calculating: sim â¡ ( A , B ) = 2 * âˆ‘ e âˆˆ A â‹‚ B â¢ ( idf e * w e ) âˆ‘ e âˆˆ A â¢ ( idf e * w e ) + âˆ‘ e âˆˆ B â¢ ( idf e * w e ) , wherein idf e indicates an IDF value for an entity e, and wherein w e indicates a weight for entity e; and producing a result indicating documents that are similar to the target document based on the calculated similarity level.
10. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for estimating a similarity level between documents, the method comprising: extracting, from a set of documents, a set of semantic entities, wherein a respective semantic entity includes a meaningful sequence of characters; determining, for the respective semantic entity, a predefined word group to which the respective semantic entity belongs, wherein the predefined word group indicates a content category associated with the respective semantic entity; computing an inverse document frequency (IDF) value for the respective semantic entity; assigning a weight to the computed IDF value based on the predefined word group to which the respective semantic entity belongs; calculating the similarity level sim(A,B) between a respective document A in the set of documents and a target document B, based on weighted IDF values associated with one or more of the extracted semantic entities, wherein calculating the similarity level involves calculating: sim â¡ ( A , B ) = 2 * âˆ‘ e âˆˆ A â‹‚ B â¢ ( idf e * w e ) âˆ‘ e âˆˆ A â¢ ( idf e * w e ) + âˆ‘ e âˆˆ B â¢ ( idf e * w e ) , wherein idf e indicates an IDF value for an entity e, and wherein w e indicates a weight for entity e; and producing a result indicating documents that are similar to the target document based on the calculated similarity level. 11. The computer-readable storage medium of claim 10 , wherein extracting the semantic entity further involves: applying a number of finite state machines configured to recognize the semantic entity as belonging to the predefined word group; searching and comparing at least one external resource to determine whether the recognized semantic entity matches an entry within the external resource; and in response to a match being found, extracting the semantic entity.