Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60.
Additional ways to cite the dataset.
Named entity recognition (NER) enables the detection of named entities (e.g. personal names, organizations, geographical locations) in the text. Stanford CoreNLP annotates tokens with named entity types such as PERSON, DATE, CITY, etc. Please note that unlike other named entity taggers used in Sparv, Stanford CoreNLP does not add structural annotations for multi-word named entities.
This analysis is used with Sparv. Check out Sparv's quick start guide to get started!
To use this analysis, add the following line under export.annotations in the Sparv corpus configuration file:
- <token>:stanford.ne_type # Named entitiy types from Stanford Parser
In order to use this annotation you need to add the following setting to your Sparv corpus configuration file:
metadata:
language: eng
For more info on how to use Sparv, check out the Sparv documentation.
Example output:
<token>The</token>
<token ne_type="NATIONALITY">Swedish</token>
<token ne_type="TITLE">chemist</token>
<token ne_type="PERSON">Alfred</token>
<token ne_type="PERSON">Bernhard</token>
<token ne_type="PERSON">Nobel</token>
<token>was</token>
<token>born</token>
<token>on</token>
<token ne_type="DATE">21</token>
<token ne_type="DATE">October</token>
<token ne_type="DATE">1833</token>
<token>in</token>
<token ne_type="CITY">Stockholm</token>
<token>.</token>
Marie-Catherine de Marneffe, Bill MacCartney and Christopher D. Manning. 2006. Generating Typed Dependency Parses from Phrase Structure Parses. In LREC 2006.