Microsoft Technology Licensing, LLC

Peide Zhong

Qi He

Dan Shacham

Uri Merhav

Fei Chen

Feng Guo

Angela Jiang

Jaewon Yang

Ji Yan

Feishe Chen

Weizhi Meng

Wei Kang

Shuai Wang

Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.

Techniques for improving standardized data accuracy

During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.

Semantic matching and retrieval of standardized entities

An online social networking system extracts terms from an unstructured job title record. The system searches a job role taxonomy database with the extracted terms to identify job roles. For each job role identified, the system extracts a plurality of additional terms appearing in the unstructured job title record. For each additional term, the system maps the additional term to a standardized modifier, thereby identifying a job seniority modifier, a job specialty modifier, a job accreditation modifier, and a job status modifier for each additional term. The system creates a multi-dimensional standardized job title for the member profile or job posting by writing the job role, the job seniority modifier, the job specialty modifier, the job accreditation modifier, and the job status modifier to a standardization record in a standardization database.

Multi-dimensional job title logical models for social network members

An online social networking system receives an unstructured job title record from a profile of a member or a job posting. The system extracts a raw job title from the unstructured job title record, and extracts a first seniority level from the raw job title. The first seniority level is a seniority modifier associated with the raw job title. The system determines a second seniority level. The second seniority level is a company seniority within the company associated with the unstructured job title record. The system determines a third seniority level. The third seniority level is a seniority score for the member or the job posting. The system compares the seniority score with a second seniority score, and communicates with the member, or transmits the job posting to the member, based on the comparison of the seniority score and the second seniority score.

Deriving multi-level seniority of social network members

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Peide Zhong