  Entity linking is the task of mapping potentially ambiguous terms in text to
their constituent entities in a knowledge base like Wikipedia. This is useful
for organizing content, extracting structured data from textual documents, and
in machine learning relevance applications like semantic search, knowledge
graph construction, and question answering. Traditionally, this work has
focused on text that has been well-formed, like news articles, but in common
real world datasets such as messaging, resumes, or short-form social media,
non-grammatical, loosely-structured text adds a new dimension to this problem.
  This paper presents Pangloss, a production system for entity disambiguation
on noisy text. Pangloss combines a probabilistic linear-time key phrase
identification algorithm with a semantic similarity engine based on
context-dependent document embeddings to achieve better than state-of-the-art
results (>5% in F1) compared to other research or commercially available
systems. In addition, Pangloss leverages a local embedded database with a
tiered architecture to house its statistics and metadata, which allows rapid
disambiguation in streaming contexts and on-device disambiguation in low-memory
environments such as mobile phones.
