Applying Dynamic Embeddings in Natural Language Processing to Analyze Text over Time
Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity of words in a large corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.
In this talk, I will introduce exponential family embeddings. Developed by Rudolph and Blei, these methods extend the idea of word embeddings to other types of high-dimensional data. I will demonstrate how they can be used to conduct advanced topic modeling on datasets that are medium-sized, which are specialized enough to require significant modifications of a word2vec model and contain more general data types (including categorical, count, continuous). I will discuss how my team implemented a dynamic embedding model using Tensor Flow and our proprietary corpus of job descriptions. Using both categorical and natural language data associated with jobs, we charted the development of different skill sets over the last 3 years. I will specifically focus the description of results on how tech and data science skill sets have developed, grown and pollinated other types of jobs over time.
Outline/Structure of the Case Study
Key Question: How have data science skills changed over time?
- Key approaches to identify changes among corpora:
- Manual Feature Extraction
- Dynamic Topic Models
- Brief introduction to the power of 'vanilla' word embeddings (i.e. GLoVE or word2vec) including:
- Semantic similarities
- Entity relationships
- Bias reflection
- Pros and cons of pretrained embeddings
- Approaches to dynamic embeddings
- Static stitched together
- Dynamic model trained together
- Key results from experiments where we implemented dynamic embedding analysis of job descriptions
- Small corpus identified gains and losses
- Large corpus identified role-dependent shifts in requirements
(1) Lessons learnt from implementing different word embedding methods (from pretrained to custom);
(2) The impact of corpus size on insights gleaned from dynamic embeddings models;
(3) How data science skills have varied across industries, functions and over time.
Intermediate level Data Scientists and Data Engineers with an interest in NLP
Prerequisites for Attendees
A basic understanding of word embeddings is helpful but not required.
schedule Submitted 2 months ago
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