In SpacyV1 it was possible to train the NER model by providing a document and a list of entity annotations in BILOU format.
However it seems as if in V2 training is only possible by providing entity annotation like this (7, 13, 'LOC'), so with enity offsets and entity tag.
Is the old way of providing the list of tokens and another list of entity tags in BILOU format still valid?
From what I gather from the documentation it looks like the nlp.update method accepts a list of GoldParse objects so I could create a GoldParse Object for each doc and pass the BILOU tags to its entities attribute. However would I loose important information by ignoring the other attributes of the GoldParse class (e.g. heads or tags https://spacy.io/api/goldparse ) or are the other attributes not needed for training the NER?
Thanks!
Yes, you can still create GoldParse objects with the BILUO tags. The main reason the usage examples show the "simpler" offset format is that it makes them slightly easier to read and understand.
If you only want to train the NER, you can now also use the nlp.disable_pipes() context manager and disable all other pipeline components (e.g. the 'tagger' and 'parser') during training. After the block, the components will be restored, so when you save out the model, it will include the whole pipeline. You can see this in action in the NER training examples.
How can you train using the GoldParse object? I've being trying for a while I've could not figure out.
Related
I'm trying to figure out how to properly markup for Relation Extraction task (I'm going to use the lstm model).
At the moment, I figured out that entities are highlighted using the
<e1>, </e1>, <e2> and </e2>
tags. And in a separate column, the class of the relationship is indicated.
But what to do in the case when in the sentence one entity has relations of the same type or different ones at once to two other entities.
An example in the image.
Or when there are four entities in one sentence and two relations are defined.
I have two options. The first is to introduce new tags
<e3>, </e3>, <e4> and </e4>
and do multi-class classification. But I haven't seen it done anywhere. The second option is to make a copy of the proposal and share the relationship in this way.
Can you please tell me how to do this markup?
This is a question regarding training models on SPACY3.x.
I couldn't find a good answer/solution on StackOverflow hence the query.
If I am using the existing model in spacy like the en model and want to add my own entities in the model and train it, let's say since I work in the biomedical domain, things like virus name, shape, length, temperature, temperature value, etc. I don't want to lose the entities tagged by Spacy like organization names, country, etc.
All suggestions are appreciated.
Thanks
There are a few ways to do that.
The best way is to train your own model separately and then combine both models in one pipeline, with one before the other. See the double NER example project for an overview of that.
It's also possible to update the pretrained NER model, see this example project. However this isn't usually a good idea, and definitely not if you're adding completely different entities. You'll run into what's called "catastrophic forgetting", where even though you're technically updating the model, it ends up forgetting everything not represented in your current training data.
I have trained model using Auto Natural Language Processing - Entity extraction. For now I have trained this model to extract single keyword under each entity from text however I want to tag single keyword under two entity to create a hierarchy. Example - For now keyword "Lazada" tagged under "Lazada_Ecommerce" however I want to tag this single keyword under two entities - sub-entity "Lazada" and main entity "Ecommerce". It would be great help if someone suggest if it is possible with Google Auto NLP-Entity Extraction model and how.
Thanks,
Satish Kumar
Data Scientist
Google NLP Entity Extraction does not support entity hierarchies. The result of a prediction includes an array of entities, corresponding to each detected entity in the text.
https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#google.cloud.automl.v1.PredictResponse
includes property 'payload' which is an array of:
https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#google.cloud.automl.v1.AnnotationPayload
Note: If a "sub-entity" can only have one "main entity", then you could manage entity hierarchies external to the model, i.e., train the model to predict "Lazada" and other sub-entities, and externally identify that "Lazada" and others belong to a main "Ecommerce" category. However, if your entity model could have a "Lazada" entity underneath multiple main entities then your current solution would be appropriate (e.g., "Lazada_Ecommerce", "Lazada_SomeOtherMainEntity", etc.).
How I can perform NER for custom named entity. e.g. If I want to identify if particular word is skill in resume. If (Java, c++) is occurring in my text i should be able to label them as skill. I don't want to use spacy with custom corpus.I want to create the dataset e.g.
words will be my features and label(skill) will be my dependent variable.
what is the best approach to handle these kinda problems.
The alternative to custom dictionaries and gazettes is to create a dataset where you assign to each word the corrisponding label. You can define a set of labels (e.g. {OTHER, SKILL}) and create a dataset with examples like:
I OTHER
can OTHER
program OTHER
in OTHER
Python SKILL
. OTHER
And with a large enough dataset you train a model to predict the corresponding label.
You can try to get a list of "coding language" synonims (or the specific skills you are looking for) from word embeddings trained on your CV corpus and use this information to automatically label other corpora. I would say that key point is to find a way to at least partially automatize the labeling otherwise you won't have enough examples to train the model on your custom NER task. Use tools like https://prodi.gy/ that reduce the labeling effort.
As features you can also use word embeddings (or other typical NLP features like n-grams, POS tag, etc. depending on the model you are using)
Another option is to apply transfer learning from other NER/NLP models and finetune them on your CV labeled dataset.
I would put more effort in creating the right dataset and then test gradually more complex models selecting what best fit your needs.
SpaCy's documentation has some information on adding new slangs here.
However, I'd like to know:
(1) When should I call the following function?
lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM], NORM_EXCEPTIONS, BASE_NORMS)
The typical usage of spaCy, according to the introduction guide here, is something as follows:
import spacy
nlp = spacy.load('en')
# Should I call the function add_lookups(...) here?
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
(2) When in the processing pipeline are norm exceptions handled?
I'm assuming a typical pipeline as such: tokenizer -> tagger -> parser -> ner.
Are norm exceptions handled right before the tokenizer? And also, how is the norm exceptions component organized with respect to the other pre-processing components such as stop words, lemmatizer (see full list of components here)? What comes before what?
Am new to spaCy and much help would be appreciated. Thanks!
The norm exceptions are part of the language data and the attribute getter (the function that takes a text and returns the norm), is initialised with the language class, e.g. English. You can see an example of this here. This all happens before the pipeline is even constructed.
The assumption here is that the norm exceptions are usually language-specific and should thus be defined in the language data, independent of the processing pipeline. Norms are also lexical attributes, so their getters live on the underlying lexeme, the context-insensitive entry in the vocabulary (as opposed to a token, which is the word in context).
However, the nice thing about the token.norm_ is that it's writeable – so you can easily add a custom pipeline component that looks up the token's text in your own dictionary, and overwrites the norm if necessary:
def add_custom_norms(doc):
for token in doc:
if token.text in YOUR_NORM_DICT:
token.norm_ = YOUR_NORM_DICT[token.text]
return doc
nlp.add_pipe(add_custom_norms, last=True)
Keep in mind that the NORM attribute is also used as a feature in the model, so depending on the norms you want to add or overwrite, you might want to only apply your custom component after the tagger, parser or entity recognizer is called.
For example, by default, spaCy normalises all currency symbols to "$" to ensure that they all receive similar representations, even if one of them is less frequent in the training data. If your custom component now overwrites "€" with "Euro", this will also have an impact on the model's predictions. So you might see less accurate predictions for MONEY entities.
If you're planning on training your own model that takes your custom norms into account, you might want to consider implementing a custom language subclass. Alternatively, if you think that the slang terms you want to add should be included in spaCy by default, you can always submit a pull request, for example to the English norm_exceptions.py.