# Improve NER label results on Non-English text

I am working on some Medieval Latin text and was using various methods of NER such as CLTK (Latin Model), Spacy (Multilingual, Italian, Spanish Model) and StanfordNER (Spanish Model). When I used the non-Latin models I used the original Latin text as the translated one was not making any sense.

Fortunately Spacy Multilingual model managed to extract all Persons and Places of the sample documents but with additional words that I am not considering them as Entities. Moreover, the labels are incorrect.

Here is an example output:

{'LOC': ['Artali', 'Artalis', 'Bruges', 'Unde'],
'MISC': ['Marianum lu Tignusu'],
'PER': ['Simone de Mazara',
'Artalem de Alagona',
'Apoca',
'Coram',
'Pero de Naso',
'Pero Caruana',
'Bartholomeo Xacara',
'Testamur',
'Artalis de Alagona',
'Melite',
'Simonis de Mazara',
'Simonem',
'Simone',
'Mariano',
'Artalis',
'Artalem',
'Simoni',
'Panormi',
'Renunciando']}


where the LOCATIONS should be: Panormi, Bruges, Melite and PERSONAL names should be all others except Unde, Apoca, Coram, Testamur, Renunciando which are neither locations nor personal names.

I was thinking of ignoring the labels and do some classification ML algorithm. The problem is that I do not have any training data available and the only possible usable corpus that I think it might be useful is Proiel treebank which labels proper nouns as NE. How would you go with such a problem?

• In a situation like this, where you have very little labeled data, it might be worth labeling some manually, either yourself or by hiting someone to do it. – Josh Friedlander Aug 9 '18 at 19:42

One approach you can take is Multi-Task Learning. This approach is a little more complicated but tackles your problem in hand.

The idea is that you train a neural network to perform different NLP tasks. For example:

• Translation
• Part-of-speech tagging
• Named entity recognition
• Chunking