PHASE I RECAP We are exploring the problem space of Named Entity Recognition (NER): processing unannotated text and extracting people, locations, and organizations. A previous post described our comparative performance evaluation of several open source and commercial NER libraries. Based upon this evaluation we determined that Amazon Comprehend (an AWS solution) performed robustly against test corpora, while the top open source solutions for the NER task were AllenNLP ELMo, Stanford NER Tagger, and NeuroNER. PURPOSE This blog post picks up where the previous post left off, building on evaluation results to explore a practical application of NER. TOOLS To facilitate this…
Evaluating Solutions for Named Entity Recognition
To gain insights into the state of the art of Named Entity Recognition (NER) solutions, Novetta conducted a quick-look study exploring the entity extraction performance of five open source solutions as well as AWS Comprehend.
BACKGROUND Named Entity Recognition (NER) and Entity Extraction are interchangeable terms that refer to the task of classifying “named entities” into pre-defined categories such as the names of persons, organizations, locations, etc.1 Let’s look at an example of how this actually works. Given a sample input: John went to the Bank of America in Washington DC An NER algorithm will read in this sentence and look to tag words it recognized as persons, organizations, locations, etc. The following represents the tagged version of the sentence: [John]PERSON went to the [Bank of America]ORGANIZATION in [Washington DC]LOCATION Such tagging is useful because…
