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View all- Al-Moslmi TGallofre Ocana ML. Opdahl AVeres C(2020)Named Entity Extraction for Knowledge Graphs: A Literature OverviewIEEE Access10.1109/ACCESS.2020.29739288(32862-32881)Online publication date: 2020
Pangloss: Fast Entity Linking in Noisy Text Environments
Authors: 
Michael Conover, Matthew Hayes, Scott Blackburn, Pete Skomoroch, Sam ShahAuthors Info & Claims
Michael Conover, Matthew Hayes, Scott Blackburn, Pete Skomoroch, Sam ShahAuthors Info & ClaimsPages 168 - 176
Abstract
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.
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- Pangloss: Fast Entity Linking in Noisy Text Environments
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Published: 19 July 2018
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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 19 - 23, 2018
London, United Kingdom
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
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View all- Al-Moslmi TGallofre Ocana ML. Opdahl AVeres C(2020)Named Entity Extraction for Knowledge Graphs: A Literature OverviewIEEE Access10.1109/ACCESS.2020.29739288(32862-32881)Online publication date: 2020
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