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Entity linking is the task of mapping potentially ambiguous terms in text totheir constituent entities in a knowledge base like Wikipedia. This is usefulfor organizing content, extracting structured data from textual documents, andin machine learning relevance applications like semantic search, knowledgegraph construction, and question answering. Traditionally, this work hasfocused on text that has been well-formed, like news articles, but in commonreal 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 disambiguationon noisy text. Pangloss combines a probabilistic linear-time key phraseidentification algorithm with a semantic similarity engine based oncontext-dependent document embeddings to achieve better than state-of-the-artresults (>5% in F1) compared to other research or commercially availablesystems. In addition, Pangloss leverages a local embedded database with atiered architecture to house its statistics and metadata, which allows rapiddisambiguation in streaming contexts and on-device disambiguation in low-memoryenvironments such as mobile phones.

Pangloss: Fast Entity Linking in Noisy Text Environments