Macau: Large-scale skill sense disambiguation in the online recruitment domain
Named entity sense disambiguation is a problem with important natural language processing applications. In the online recruitment industry, normalization and recognition of occupational skills play a key role in linking the right candidate with the right job. The disambiguation of multisense skills will help improve this normalization and recognition process. In this paper we discuss an automatic large-scale system to identify and disambiguate multi-sense skills, including: (1) Feature Selection: employing word embedding to quantify the skills and their contexts into vectors; (2) Clustering: applying Markov Chain Monte Carlo (MCMC) methods to aggregate vectors into clusters that represent respective senses; (3) Large-scale: implementing parallelization to process text blobs on a large-scale; (4) Pruning: cluster cleaning by analyzing intra-cluster cosine similarities. Based on experiments on sample datasets, the MCMC-based clustering algorithm outperforms other clustering algorithms for the disambiguation problem. Also based on data-driven in-house evaluations, our disambiguation system achieves 84% precision.