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CICLING
2010
Springer

A Distributional Semantics Approach to Simultaneous Recognition of Multiple Classes of Named Entities

11 years 2 days ago
A Distributional Semantics Approach to Simultaneous Recognition of Multiple Classes of Named Entities
Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing tools apply features and rules mainly at the word level or use lexicons. Recent advances in distributional semantics allow us to efficiently create paradigmatic models that encode word order. We used Sahlgren et al's permutation-based variant of the Random Indexing model to create a scalable and efficient system to simultaneously recognize multiple entity classes mentioned in natural language, which is validated on the GENIA corpus which has annotations for 46 biomedical entity classes and supports nested entities. Using distributional semantics features only, it achieves an overall micro-averaged Fmeasure of 67.3% based on fragment matching with performance ranging from 7.4% for "DNA substructure" to 80.7% for "Bioentity".
Siddhartha Jonnalagadda, Robert Leaman, Trevor Coh
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where CICLING
Authors Siddhartha Jonnalagadda, Robert Leaman, Trevor Cohen, Graciela Gonzalez
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