lemmatizer | English word lemmatizer | Natural Language Processing library

What you are getting is an internal error from spaCy. You use the en_core_web_trf model provided by spaCy. It's not even a third-party model. It seems to be completely internal to spaCy.

You could try upgrading spaCy to the latest version.

The registry name scorers appears to be valid (at least as of spaCy v3.0). See this table: https://spacy.io/api/top-level#section-registry

The page describing the model you use: https://spacy.io/models/en#en_core_web_trf

The spacy.load() function documentation: https://spacy.io/api/top-level#spacy.load

You mean how to instead of printing get all the values into a list which you can then print?

You can do that with a list comprehension:

Here's what I came up with:

First problem that can be identified is that your convert_lower_case returns something different than it accepts - which could be perfectly fine, if treated properly. But you keep treating your data as a string, which it no longer is after data = convert_lower_case(data)

"But it looks like a string when I print it" - yeah, but it isn't a string. You can see that if you do this:

It isn't possible to do this. The NER model is classifying each token/span between all the labels it knows about, and the knowledge is not separable.

Additionally, the NER component requires a tok2vec. Depending on the pipeline architecture you may be able to disable the top-level tok2vec. (EDIT: I incorrectly stated the top-level tok2vec was required for the small English model; it is not. See here for details.)

It may be possible to train a smaller model that only recognizes GPEs with similar accuracy, but I wouldn't be too optimistic about it. It also wouldn't be faster.

1. Is there a practical use case to perform lemmatization on the tokenized data and then stem that lemmatized data or vice versa

2. Does the idea of stemming the lemmatized data or vice versa make any sense theoretically, or is it completely incorrect.

Regarding (1): Lemmatisation and stemming do essentially the same thing: they convert an inflected word form to a canonical form, on the assumption that features expressed through morphology (such as word endings) are not important for the use case. If you are not interested in tense, number, voice, etc, then lemmatising/stemming will reduce the number of distinct word forms you have to deal with (as different variations get folded into one canonical form). So without knowing what you want to do exactly, and whether morphological information is relevant to that task, it's hard to answer.

Lemmatisation is a linguistically motivated procedure. Its output is a valid word in the target language, but with endings etc removed. It is not without information loss, but there are not that many problematic cases. Is does a third person singular auxiliary verb, or the plural of a female deer? Is building a noun, referring to a structure, or a continuous form of the verb to build? What about housing? A casing for an object (such as an engine) or the process of finding shelter for someone?

Stemming is a less resource intense procedure, but as a trade-off it works with approximations only. You will have less precise results, which might not matter too much in an application such as information retrieval, but if you are at all interested in meaning, then it is probably too coarse a tool. Its output also will not be a word, but a 'stem', basically a character string roughly related to those you get when stemming similar words.

Re (2): no, it doesn't make any sense. Both procedures attempt the same task (normalising inflected words) in different ways, and once you have lemmatised, stemming is pointless. And if you stem first, you generally do not end up with valid words, so lemmatisation would not work anyway.

In order to get the representation of a document (represented as a bag-of-words) from a trained LsiModel as a vector of topics, you use Python dict-style bracket-accessing (model[bow]).

For example, to get the topics for the 1st item in your training data, you can use:

For a best output, you can use spacy

I got where the problem is, the dataframes are storing these arrays as a string. So, the lemmatization is not working. Also note that, it is from the spell_eng part.

I have written a solution, which is a slight modification for your code.