How to use LLMs for text extraction and annotation

Posted on by Panther

How to Use Language Model Libraries (LLMs) for Text Extraction and Annotation

Language Model Libraries (LLMs) are powerful tools for text extraction and annotation. They leverage pre-trained language models to perform a wide range of natural language processing tasks, such as named entity recognition, part-of-speech tagging, and dependency parsing. In this tutorial, we’ll explore how to use LLMs for text extraction and annotation.

Prerequisites

To follow along with this tutorial, you’ll need:

  • Basic knowledge of Python programming language
  • Familiarity with natural language processing concepts
  • Python 3.6 or higher installed on your machine

Step 1: Install LLMs

To get started, you’ll need to install an LLM library. There are several popular options available, such as Hugging Face’s Transformers library and SpaCy’s implementation of LLMs. For this tutorial, we’ll use SpaCy.

You can install SpaCy by running the following command:

pip install spacy

After installing SpaCy, you’ll also need to download a language model. SpaCy provides a variety of pre-trained models for different languages. These models are trained on large corpora and can be used to perform various natural language processing tasks.

For example, to download the English language model, you can run the following command:

python -m spacy download en_core_web_sm

Step 2: Load the Language Model

Once you have installed SpaCy and downloaded a language model, you can load the model into your Python script or interactive session. The following code snippet demonstrates how to load the English language model:

import spacy

nlp = spacy.load("en_core_web_sm")

Step 3: Text Extraction

Now that we have loaded the language model, we can use it to extract useful information from a given text. SpaCy’s language models provide a wide range of annotations, including named entities, part-of-speech tags, and syntactic dependencies.

To extract these annotations, we need to process the text using the loaded model. Here’s an example of how to process a text string using SpaCy:

text = "Apple is looking at buying U.K. startup for $1 billion"

doc = nlp(text)

After processing the text, you can access the extracted annotations from the doc object.

For example, to extract the named entities from the text, you can iterate over the ents attribute of the doc object:

for entity in doc.ents:
    print(entity.text, entity.label_)

This will print the named entities along with their corresponding entity types.

Similarly, you can access other annotations such as part-of-speech tags and syntactic dependencies using the respective attributes of the Token objects in the doc object.

for token in doc:
    print(token.text, token.pos_, token.dep_)

Step 4: Text Annotation

LLMs can also be used to annotate texts with custom information. You can add your own annotations to the Token objects of a Doc object.

For example, let’s say we want to annotate the sentiment of each sentence in a given text. We can define a custom attribute on the Token objects called sentiment, and assign a sentiment value to each token.

from spacy.tokens import Token

Token.set_extension("sentiment", default=None)

text = "I love SpaCy. It's an amazing library."

doc = nlp(text)

for sentence in doc.sents:
    sentence_sentiment = 0

    for token in sentence:
        if token.text.lower() in ["love", "amazing"]:
            sentence_sentiment += 1
        elif token.text.lower() in ["hate", "terrible"]:
            sentence_sentiment -= 1

    for token in sentence:
        token._.sentiment = sentence_sentiment / len(sentence)

In this example, we iterate over each sentence in the text and calculate a sentiment value for each sentence. Then, we assign the sentiment value to each token within the sentence using the custom attribute sentiment.

After annotating the text, you can access the custom annotations using the custom attribute, _.attribute_name.

for token in doc:
    print(token.text, token._.sentiment)

This will print the sentiment value for each token in the text.

Conclusion

LLMs are powerful tools for text extraction and annotation. In this tutorial, we learned how to use LLMs to extract annotations from text using SpaCy, as well as how to add custom annotations to texts. With these techniques, you can leverage the power of LLMs to perform a wide range of natural language processing tasks.

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