I'm trying to extract locations from blobs of text (NER/IE) and have tried many solutions all which are far too innaccurate spacy, Stanford etc etc.
All really are only about 80-90% accurate on my dataset (spacy was like 70%), another problem I'm having is not having a probability that means anything for these entities so I don't know confidence and can't proceed accordingly.
I tried a super naive approach of splitting my blobs into singular words then extracting surrounding context as features, also used a location placename lookup (30/40k location placenames) as a feature aswell. Then I used just a classifier(XGDBoost) and the results where much better once I trained the classifier on about 3k manually labelled datapoints (100k total only 3k where locations). 95% precision for states/countries and about 85% for cities.
This approach sucks obviously but why is it outperforming everything I have tried? I think the black box approach to NER just isn't working for my data problem, I tried spacy custom training and it really just didn't seem like it was going to work. Not having a confidence in the entity is kind of killer also as the probability they give you for that is almost meaningless.
Is there someway I can approach this problem a little better to improve my results even more? shallow nlp for like 2/3/4-grams? Another problem I have with my approach is the output of the classifier isnt some sequential entity, its literally just classified word blobs which somehow need to be clustered back into one entity i.e : -> San Francisco, CA is just 'city','city', '0','state' with no concept of them being the same entity
spacy example:
example blob:
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and the results:
('US', 'GPE')
('US', 'GPE')
('US', 'GPE')
('the', 'GPE')
('United', 'GPE')
('States', 'GPE')
('Jesus', 'GPE')
('Christ', 'GPE')
('Pontius', 'GPE')
('Pilate', 'GPE')
('Faith', 'GPE')
('A', 'GPE')
Even the best Deep Learning based NER systems only achieve an F1 of 92.0 these days. Deep Learning based systems (CNN-BiLSTM-CRF) should outperform Stanford CoreNLP's plain CRF sequence tagger. Recently there have been even more advancements involving integrating language models. You might want to look at AllenNLP.
But if you want super high accuracy like 99.0%, you're going to have integrate rule-based approaches for the time being.
I think rule-based processing could be helpful. For instance, you can write a pattern that says "city city O , state" should be merged together into one entity. Also, you might want to consider discarding entities that don't appear in your dictionary of location/places. Or discard entities that aren't in a location dictionary but are in another type. But I find it hard to believe many unknown string sequences are location place names you care about extracting. I would think people names are the most likely to be outside of dictionaries.
UIUC's NLP tools have some dictionaries in them if you download their software.
When running StanfordCoreNLP, using the ner,regexner,entitymentions annotators will allow automatic grouping together of consecutive NE tags into entities. Full info on the pipeline here: https://stanfordnlp.github.io/CoreNLP/cmdline.html
Also, remember the out-of-the-box versions of these systems are typically trained on news articles from the last 15 years. Retraining on data closer to your set is essential. Ultimately you might be best off just writing some rules that do dictionary based extraction.
You can look into Stanford CoreNLP's TokensRegex and RegexNER functionality to see how to use Stanford CoreNLP for that purpose.
TokensRegex: https://nlp.stanford.edu/software/tokensregex.html RegexNER: https://nlp.stanford.edu/software/regexner.html