How to Extract Handcrafted Features in Computational Linguistics using LFTK

Oct 27, 2020 | Data Science

Welcome to the world of computational linguistics where we’ll explore LFTK, a Python research package designed to extract handcrafted features that help us analyze and enhance textual data. This guide will walk you through the installation, usage, and troubleshooting tips associated with LFTK to set you on your path of linguistic discovery!

What is LFTK?

LFTK, short for Linguistic Feature Toolkit, operates like a detailed map in a vast forest of linguistic data. Imagine you are an explorer trying to find the fastest path—a GPS of linguistic features guiding you through the collection of handcrafted tools that can analyze your text. With over 200 features to choose from, extracting information such as readability scores, word difficulty, and noun counts has never been easier.

Installation

To embark on this adventure, you’ll need to install LFTK and its dependencies. Here’s how you can do it:

  • Open your terminal and execute the following command to install LFTK via pip:
  • pip install lftk
  • Next, install spaCy and download a pre-trained spaCy pipeline (we’ll use “en_core_web_sm” for English):
  • pip install spacy
    python -m spacy download en_core_web_sm

Usage

Now that we have everything installed, let’s extract some handcrafted features!

Consider this process like baking a cake. You need a recipe representing your linguistic approach, ingredients are the text and features you want to analyze, and the oven is the LFTK extractor that will combine them beautifully.

  • First, import the necessary libraries:
  • import spacy
    import lftk
  • Next, load your spaCy pipeline:
  • nlp = spacy.load("en_core_web_sm")
  • Create a spaCy doc object with your text:
  • doc = nlp("I love research but my professor is strange.")
  • Now, initialize the LFTK extractor:
  • LFTK = lftk.Extractor(docs = doc)
  • Customize your extraction settings:
  • LFTK.customize(stop_words=True, punctuations=False, round_decimal=3)
  • Finally, specify which features you’d like to extract:
  • extracted_features = LFTK.extract(features = [a_word_ps, a_kup_pw, n_noun])
  • To see the results, print them out:
  • print(extracted_features)

Common Troubleshooting Tips

As with any journey, you may encounter roadblocks. Here are some troubleshooting tips to help you navigate:

  • If you run into installation errors, try updating pip before installing LFTK and spaCy with the command:
  • pip install --upgrade pip
  • If spaCy models are not recognized, ensure you’ve downloaded the correct model by double-checking your command.
  • For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With this guide, you are now equipped to harness the power of LFTK to delve deep into the intricacies of linguistic features. Happy exploring!

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