Domain name classification systems and methods
Disclosed is a domain engineering analysis solution that determines relevance of a domain name to a brand name in which a domain name, brand name, and identification of a substring of the domain name may be provided to or obtained by a computer embodying a domain engineering analyzer. A list of features may be determined. The list of features may include a lexicon, or a set of key-value pairs that encode information about terms included as substrings in the domain name. Determining the features may include obtaining a language model for each term, analyzing a cluster of language models closest to the obtained language model, and determining and scoring a relevance of each term to the brand name. The determined relevance and score of each term may be provided to a client. This relevance analysis can be dynamically applied in an online process or proactively applied in an offline process.
1. A method, comprising:
obtaining, by a computer, a domain name, a seed value, and an identification of a substring of the domain name that is relevant match to the seed value;
determining, by the computer, key-value pairs that encode information about terms in substrings of the domain name, the terms including a term in the substring of the domain name, wherein the determining comprises:
obtaining a language model for the term in the substring of the domain name;
analyzing a cluster of language models closest to the language model for the term in the substring of the domain name, wherein the analyzing the cluster of language models comprises analyzing a plurality of language models within a predetermined threshold distance from the language model for the term in the substring of the domain name; and
determining, based on the analyzing, a relevance of the term in the substring of the domain name to the seed value; and
providing, by the computer, the relevance of the term in the substring of the domain name to the seed value to a computing device.
2. The method according to claim 1 , wherein each of the plurality of language models is associated with a word that does not match the term in the substring of the domain name.
3. The method according to claim 1 , wherein the information about the terms in the substrings of the domain name comprises values of edit distances.
4. The method according to claim 1 , wherein the language model for the term in the substring of the domain name comprises a word vector for the term and wherein the relevance of the term in the substring of the domain name to the seed value is determined utilizing the word vector for the term.
5. The method according to claim 1 , wherein the key-value pairs include one or more index values indicating a starting location and an ending location of the substring of the domain name.
6. The method according to claim 1 , wherein the seed value comprises a brand name.
7. The method according to claim 6 , wherein determining the relevance of the term in the substring of the domain name to the seed value comprises generating a score that indicates how relevant the term in the substring of the domain name is to the domain name and to the brand name.
8. A system, comprising:
a processor;
a non-transitory computer-readable medium; and
instructions stored on the non-transitory computer-readable medium and translatable by the processor for:
obtaining a domain name, a seed value, and an identification of a substring of the domain name that is relevant match to the seed value;
determining key-value pairs that encode information about terms in substrings of the domain name, the terms including a term in the substring of the domain name, wherein the determining comprises:
obtaining a language model for the term in the substring of the domain name;
analyzing a cluster of language models closest to the language model for the term in the substring of the domain name, wherein the analyzing the cluster of language models comprises analyzing a plurality of language models within a predetermined threshold distance from the language model for the term in the substring of the domain name; and
determining, based on the analyzing, a relevance of the term in the substring of the domain name to the seed value; and
providing the relevance of the term in the substring of the domain name to the seed value to a computing device.
9. The system of claim 8 , wherein each of the plurality of language models is associated with a word that does not match the term in the substring of the domain name.
10. The system of claim 8 , wherein the information about the terms in the substrings of the domain name comprises values of edit distances.
11. The system of claim 8 , wherein the language model for the term in the substring of the domain name comprises a word vector for the term and wherein the relevance of the term in the substring of the domain name to the seed value is determined utilizing the word vector for the term.
12. The system of claim 8 , wherein the key-value pairs include one or more index values indicating a starting location and an ending location of the substring of the domain name.
13. The system of claim 8 , wherein the seed value comprises a brand name.
14. The system of claim 13 , wherein determining the relevance of the term in the substring of the domain name to the seed value comprises generating a score that indicates how relevant the term in the substring of the domain name is to the domain name and to the brand name.
15. A computer program product comprising a non-transitory computer-readable medium storing instructions translatable by a processor for:
obtaining a domain name, a seed value, and an identification of a substring of the domain name that is relevant match to the seed value;
determining key-value pairs that encode information about terms in substrings of the domain name, the terms including a term in the substring of the domain name, wherein the determining comprises:
obtaining a language model for the term in the substring of the domain name;
analyzing a cluster of language models closest to the language model for the term in the substring of the domain name, wherein the analyzing the cluster of language models comprises analyzing a plurality of language models within a predetermined threshold distance from the language model for the term in the substring of the domain name; and
determining, based on the analyzing, a relevance of the term in the substring of the domain name to the seed value; and
providing the relevance of the term in the substring of the domain name to the seed value to a computing device.
16. The computer program product of claim 15 , wherein each of the plurality of language models is associated with a word that does not match the term in the substring of the domain name.
17. The computer program product of claim 15 , wherein the information about the terms in the substrings of the domain name comprises values of edit distances.
18. The computer program product of claim 15 , wherein the language model for the term in the substring of the domain name comprises a word vector for the term and wherein the relevance of the term in the substring of the domain name to the seed value is determined utilizing the word vector for the term.
19. The computer program product of claim 15 , wherein the key-value pairs include one or more index values indicating a starting location and an ending location of the substring of the domain name.
20. The computer program product of claim 15 , wherein the seed value comprises a brand name and wherein determining the relevance of the term in the substring of the domain name to the seed value comprises generating a score that indicates how relevant the term in the substring of the domain name is to the domain name and to the brand name.