A comparison of libraries for named entity recognition

What’s your favourite thing about SpaCy? Mine’s SpaCy.

I read and summarise software engineering papers for fun, and today we’re having a look at A replicable comparison study of NER software: StanfordNLP, NLTK, OpenNLP, SpaCy, GATE (2019) by Schmitt et al.

Why it matters

If you need NER, there’s no need to implement it yourself. There are several popular libraries that can do this for you nowadays. Five of these libraries, Stanford CoreNLP, NLTK, OpenNLP, SpaCy, and GATE, were already mentioned in the title.

Which library is right for you depends on various criteria, like its performance, cost, documentation, license, and the programming language in which it is implemented.

Many of these libraries have been evaluated in comparison studies, but sadly not in a way that makes it easy to compare findings.

How the study was conducted

This paper describes a comparison between the five aforementioned NER libraries, in a sufficiently clear and complete way, so that its results can be replicated.

The process looks roughly like this:

Selection of two corpora that are not domain-specific, freely available, and in English: the Groningen Meaning Bank (GMB) and the CoNLL 2003 corpus.

Selection of five NER libraries that are free and open-source software, well-documented, available for Linux, and can recognise at least three types of entities: persons, organisations, and locations.

Comparison of each NER library’s generated NER annotations with annotations in the “gold data”, which contains the annotations that we’d expect. This is done by computing the precision, recall, and F-score for each library.

What discoveries were made

The table below shows the results of the comparison. Don’t worry too much about its size and all the numbers, I’ve included a hangover-proof summary below the table.

Library	Entity	Precision	Recall	F-score	Precision	Recall	F-score
		CoNLL 2003			GMB
Stanford NLP	Location	91.30	88.73	90.00	83.10	63.64	72.08
	Organisation	86.32	80.92	83.53	71.40	47.42	56.99
	Person	92.72	82.68	87.41	78.59	84.70	81.53
	Overall	90.06	73.67	81.05	79.81	63.74	70.88
NLTK	Location	52.47	65.47	58.26	77.13	77.10	77.12
	Organisation	36.20	24.80	29.44	42.06	35.54	38.53
	Person	61.09	66.11	63.50	38.07	55.87	45.28
	Overall	51.78	45.56	48.47	60.96	63.91	62.40
GATE	Location	59.63	78.63	67.82	79.03	48.16	59.85
	Organisation	50.58	21.29	29.96	45.08	37.68	41.05
	Person	69.53	62.67	65.92	46.53	53.70	49.86
	Overall	61.48	47.44	53.55	61.72	46.78	53.22
OpenNLP	Location	76.54	52.22	62.08	84.34	45.84	59.40
	Organisation	38.06	14.87	21.39	59.27	30.64	40.39
	Person	83.94	37.17	51.52	62.34	41.98	50.17
	Overall	68.68	30.44	42.18	37.35	41.71	39.41
SpaCy	Location	73.38	75.36	74.36	77.04	56.64	65.28
	Organisation	40.95	36.24	38.45	41.20	36.50	38.70
	Person	66.89	56.22	61.09	67.41	69.14	68.27
	Overall	60.94	49.01	54.33	66.15	54.32	59.66

Stanford NLP’s library is the only one that has (somewhat) high scores and blows the other libraries out of the water. The other four libraries have a roughly similar level of performance.

Note that Stanford NLP’s library performs especially well on the CoNLL 2003 dataset. This is because it comes with a classifier that was partially trained on CoNLL 2003! The scores for GMB are therefore more likely to be representative for real-world texts.

The results for Stanford NLP are similar to those from other studies. However, the accuracy for three of the other libraries (NLTK, GATE, and OpenNLP) (*) may differ as much as 66% from the values reported in existing studies. It is not clear what causes such huge discrepancies.

(*) Apparently there weren’t any studies that evaluated SpaCy’s performance