Natural Language Processing (NLP) can feel like venturing into a dense forest of words and meanings; however, the PreNLP library acts as your guiding trail. In this blog, we will go through the process of setting up and utilizing this powerful library for your NLP projects. Ready to navigate? Let’s get started!

Installation Requirements

Before diving into the usage, let’s ensure you have all the prerequisites in place:

• Python version = 3.6
• Mecab Morphological Analyzer for Korean (only on Mac OS):

  sh install_mecab.sh

  Before running the above command, make sure to set the environment variables:

  export MACOSX_DEPLOYMENT_TARGET=10.10

  CFLAGS=-stdlib=libc++

• Install Visual Studio C++ for Windows users.
• C++ Build tools for fastText (g++ = 4.7.2 or clang = 3.3)

Installing PreNLP

The PreNLP library can be installed easily using pip:

pip install prenlp

Using PreNLP

Now that we have the library installed, let’s explore its capabilities.

Data Loading

The PreNLP library provides access to popular datasets for various NLP tasks. These datasets are organized in the .data directory, including:

• Sentiment Analysis: IMDb, NSMC
• Language Modeling: WikiText-2, WikiText-103, WikiText-ko, NamuWiki-ko

Example: Loading WikiText-2

Here’s how you can load the WikiText-2 dataset:

wikitext2 = prenlp.data.WikiText2()
len(wikitext2)  # Returns: 3
train, valid, test = prenlp.data.WikiText2()
train[0] = "Valkyria Chronicles III"

Normalizing Text

PreNLP enables frequent normalization of text, such as URLs, HTML tags, emails, and phone numbers. Here’s a quick overview:

from prenlp.data import Normalizer

normalizer = Normalizer(
    url_repl=[URL],
    tag_repl=[TAG],
    emoji_repl=[EMOJI],
    email_repl=[EMAIL],
    tel_repl=[TEL],
    image_repl=[IMG]
)

normalized_text = normalizer.normalize("Contact me at lyeoni.g@gmail.com")

Using Tokenizers

The library comes equipped with several tokenizers to process text efficiently, such as SentencePiece, NLTK MosesTokenizer, and Mecab.

For instance, let’s say SentencePiece is like baking cookies. You start with all your ingredients (the text) put together, and later you cut them into cookie shapes (tokens). Here’s how you can train SentencePiece:

from prenlp.tokenizer import SentencePiece

SentencePiece.train(input="corpus.txt", model_prefix="sentencepiece", vocab_size=10000)
tokenizer = SentencePiece.load("sentencepiece.model")
tokenizer.tokenize("Time is the most valuable thing a man can spend.")

Troubleshooting Tips

• If you encounter issues while installing PreNLP, double-check your Python version and ensure all required dependencies are installed.
• For normalization problems, ensure that the inputs provided to the Normalizer are in the correct format.
• When using tokenizers, verify that the input text is pre-processed correctly to avoid unexpected tokenization issues.

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Conclusion

With the PreNLP library, you have a versatile toolkit at your disposal for tackling various NLP challenges. By following the installation process, leveraging the datasets, and normalizing and tokenizing your text efficiently, you’ll be well on your way to crafting effective natural language processing solutions.

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.