NLP, Recruitment, AI - Automated Matching :) Eightfold - Analysis

A ton of zero-shot / few shot / data labeling to customize and build models for

Some of the Key features / NLP overlaps

Custom Entity Recognition

• Education
• Place
• Company 
• Domain
• Skills Extraction

Custom Embedding for

• Similar Projects Search
• Project Summarization
• Creating custom embedding for each domain

NLP + Ranking

• Ranking and retrieving based on location/salary/education
• Skill Distribution / Contribution
• Domain Extraction from Company names

Vision

• OCR + Vision for Content Retrieval

Search / Retrieval

• Vector database for Finetuning/indexing vector databases to search and retrieve the closest matches

From JD

• Familiar with Language models, and transformers like BERT, GPT-3, T-5 etc.
• Prior experience building and deploying machine learning models in production at scale
• Familiarity with MLOps tools and pipelines (MLflow, Metaflow).
• Integration with Workday, SuccessFactors, Taleo, PeopleSoft, iCIMS, SmartRecruiters,
• REST APIs, microservices, data ingestion and processing systems, and distributed systems.

Ref - Link1, Link2, Link3, Link4, Link5

Map NLP Possibilities to Applied use cases

Ref - Link

Construction of English Resume Corpus and Test with Pre-trained Language Models

A Few-shot Approach to Resume Information Extraction via Prompts

Keep Exploring!!!

Topic Modelling - LDA, LSA

• LDA stands for Latent Dirichlet Allocation, and it is a type of topic modeling algorithm
• LDA was developed in 2003 by researchers David Blei, Andrew Ng and Michael Jordan
• LDA is based on a Bayesian framework. This allows the model to infer topics based on observed data (words) through the use of conditional probabilities
• The main difference between LSA and LDA is that LDA assumes that the distribution of topics in a document and the distribution of words in topics are Dirichlet distributions. LSA does not assume any distribution and therefore, leads to more opaque vector representations of topics and documents
• Latent Semantic Analysis or Latent Semantic Indexing – Uses Singular Value Decomposition (SVD) on the Document-Term Matrix
• In practice, LSA is much faster to train than LDA, but has lower accuracy.

Example

Keep Thinking!!!