I wrote a small program to extract language entity from a text. I am using 'en_core_web_sm' but it does detect anything other than DATE from following sentence.
From spacy doc https://spacy.io/models/en, I can see that 'en_core_web_sm' support LANGUAGE entity.
I think thatI am doing some obvious mistake. Could someone please point out what am I doing wrong?
nlp2 = spacy.load("en_core_web_sm")
test_text = "korean chinese english spanish 2019-2-13 india america 2 years 6 months united states"
doc2 = nlp2(test_text)
for ent in doc2.ents:
print(ent.label_, ent.text)
print("\n")
Output
DATE 2 years 6 months
Which version of spaCy are you using? If I run this exact code snippet with the current latest version, 2.2.4, I get this print out:
NORP korean
NORP chinese
LANGUAGE english
GPE india
GPE america
DATE 2 years 6 months
As you can see, the pre-trained model en_core_web_sm does indeed recognise LANGUAGE entities.
As a quick tip: spaCy's NER module works better on actual sentences, which is what it was trained on. From my results, you see that it works also on non-grammatical sequences of words, like in your input, but it will make more mistakes because there is no grammatical context.
Related
Why is it that spacy transformer models for languages like spanish (es_dep_news_trf) don't do named entity recognition.
However, for english (en_core_web_trf) it does.
In code:
import spacy
nlp=spacy.load("en_core_web_trf")
doc=nlp("my name is John Smith and I work at Apple and I like visiting the Eiffel Tower")
print(doc.ents)
(John Smith, Apple, the Eiffel Tower)
nlp=spacy.load("es_dep_news_trf")
doc=nlp("mi nombre es John Smith y trabajo en Apple y me gusta visitar la Torre Eiffel")
print(doc.ents)
()
Why doesn't spanish extract entities but english does?
It has to do with the available training data. ner is only included for the trf models if the training data has NER annotation on the exact same data as for tagging and parsing.
Training trf models on partial annotation does not work well in practice and an independent NER component (as in the CNN pipelines) would mean including an additional transformer component in the pipeline, which would make the pipeline a lot larger and slower.
The spaCy models vary with regards to which NLP features they provide - this is just a result of how the respective authors created/trained them. I.e., https://spacy.io/models/en#en_core_web_trf lists "ner" in its components, but https://spacy.io/models/es#es_dep_news_trf does not.
The Spanish https://spacy.io/models/es#es_core_news_lg (as well the two smaller variants) does list "ner" in its components, so they show named entities:
>>> import spacy
>>> nlp=spacy.load("es_core_news_sm")
>>> doc=nlp("mi nombre es John Smith y trabajo en Apple y me gusta visitar la Torre Eiffel")
>>> print(doc.ents)
(John Smith, Apple, Torre Eiffel)
I have a dataset of articles containing french and english content. My final goal is to have similar articles that are in same cluster.
What I did first is word embeddings with fasttext. So I have word embeddings for french and another one for english content.
How can I do next to make a only one clustering with these 2 embeddings ? Is there a good practice ?
How can i generate non-english (french , spanish , italian ) word embedding from english word embedding ?
What are the best ways to generate high quality word embedding for non - english words .
Words may include (samsung-galaxy-s9)
How can i generate non-english (french , spanish , italian ) word embedding from english word embedding ?
You can't really. Unless you have words which mean exactly the same. If you have know the french word for king, queen, woman and man, you can give those words the embedding of the exact same word in english. They will show the same syntactic and semantic properties that the english words do. But you can't really use the English embeddings to make embeddings for different languages.
What are the best ways to generate high quality word embedding for non - english words
English words and non-english words can be treated the same way. Represent your non english words as strings/tokens and train a w2v model. Use gensim for this. You'll have to find a huge corpus for the language you want. Then you will have to train your model with this huge corpus for a few epochs. Done. Alternatively, look for pre existing models in your required language.
Words may include (samsung-galaxy-s9)
Unless your corpus has words like "samsung-galaxy-s9", your model won't know what it means. Use a corpus which might have more words in the domain you're hoping to use the embeddings for.
For non-english words, you can try to use a bilingual dictionary to translate English words with embedding vectors.
You need a large corpus to generate high-quality word embeddings. For non-english, you need to add the bilingual constraints into the original w2v loss with the input of bilingual corpora.
You can regard the compound word as a whole word or split it according to your applications.
Is it by design that the english language model performs better on german salution entities than the german model?
# pip install spacy
# python -m spacy download en
# python -m spacy download de
nlp = spacy.load('en')
# Uncomment line below to get less good results
# nlp = spacy.load('de')
# Process text
text = (u"Das Auto kauft Herr Müller oder Frau Meier, Frank Muster")
doc = nlp(text)
# Find named entities
for entity in doc.ents:
print(entity.text, entity.label_)
expected result if using nlp = spacy.load('en'). All three PERSON is returned
Das Auto ORG
Herr Müller PERSON
Frau Meier PERSON
Frank Muster PERSON
unexpected result if using nlp = spacy.load('de'). Only one of three PERSON is returned
Frank Muster PERSON
Info about spaCy
spaCy version:** 2.0.12
Platform:** Linux-4.17.2-1-ARCH-x86_64-with-arch-Arch-Linux
Python version:** 3.6.5
Models:** en, de
It's not by design, but it's certainly possible that this is a side-effect of the training data and the statistical predictions. The English model is trained on a larger NER corpus with more entity types, while the German model uses NER data based on Wikipedia.
In Wikipedia text, full names like "Frank Muster" are quite common, whereas things like "Herr Muster" are typically avoided. This might explain why the model only labels the full name as a person and not the others. The example sentence also makes it easy for the English model to guess correctly – in English, capitalization is a much stronger indicator of a named entity than it is in German. This might explain why the model consistently labels all capitalised multi-word spans as entities.
In any case, this is a good example of how subtle language-specific or stylistic conventions end up influencing a model's predictions. It also shows why you almost always want to fine-tune a model with more examples specific to your data. I do think that the German model will likely perform better on German texts overall, but if references like "Herr Müller" are common in your texts, you probably want to update the model with more examples of them in different contexts.
I am trying to figure out if I trust SpaCy's similarity function and I am getting confused. Here's my toy example:
import spacy
nlp = spacy.load('en')
doc1 = nlp(u'Unsalted butter')
doc2 = nlp(u'babi carrot peel babi carrot grim french babi fresh babi roundi fresh exot petit petit peel shred carrot dole shred')
doc1.similarity(doc2)
I get the similarity of 0.64. How can it be this high for two sentences with no overlapping tokens? Could someone please explain this to me? Thank you!
The problem is that you are using en model which is most likely linked to en_core_web_sm.
The en_core_web_sm model doesn't have pretrained glove vectors, so you are using the vectors produced by NER, PoS and DEP tagger to compute similarity.
These vectors encode structural information, e.g. having the same PoS tag or DEP role in the sentence. There is no semantic information encoded in these vectors, so the result you are getting is as expected, weird.
Have also a look here.