Named Entity Recognition (NER) System

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A Named Entity Recognition (NER) System is a mention recognition system (that applies a named entity recognition algorithm to solve a named entity recognition task.



References

2016

  • https://github.com/glample/tagger
    • QUOTE: … NER Tagger is an implementation of a Named Entity Recognizer that obtains state-of-the-art performance in NER on the 4 CoNLL datasets (English, Spanish, German and Dutch) without resorting to any language-specific knowledge or resources such as gazetteers. Details about the model can be found at: http://arxiv.org/abs/1603.01360 ...

2011

2009

2007

2002

  • (Roth & Yih, 2002) ⇒ Dan Roth, and Wen-tau Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2002).
    • QUOTE: In all earlier works we know of, the tasks of identifying entities and relations were treated as separate problems. The common procedure is to first identify and classify entities using a named entity recognizer and only then determine the relations between the entities. However, this approach has several problems. First, errors made by the named entity recognizer propagate to the relation classifier and may degrade its performance significantly. For example, if “Boston” is mislabeled as a person, it will never be classified as the location of Poe’s birthplace. Second, relation information is sometimes crucial to resolving ambiguous named entity recognition. For instance, if the entity “JFK” is identified as the victim of the assassination, the named entity recognizer is unlikely to misclassify it as a location (e.g. JFK airport).

1999