Parsing CoNLL-U Files with LangChain CoNLLULoader

When working with linguistic data and natural language processing tasks, you may encounter files in CoNLL-U format, which is a standardized format for annotating text with grammatical and syntactic information. LangChain provides the CoNLLULoader to help you work with these files in your applications.

What is CoNLLULoader?

CoNLLULoader is a document loader class in LangChain designed specifically for parsing CoNLL-U formatted files. CoNLL-U is a tab-separated format used for linguistic annotations, containing information about tokens, parts of speech, syntactic dependencies, and more. The loader extracts the text content from these files while preserving sentence boundaries.

Reference

Here are the main methods available in CoNLLULoader:

How to Use CoNLLULoader

Basic Usage

The most straightforward way to use CoNLLULoader is to load a CoNLL-U file and convert it into a Document object:

from langchain_community.document_loaders import CoNLLULoader

# Initialize the loader with your CoNLL-U file
loader = CoNLLULoader("path/to/your/file.conllu")

# Load the document
documents = loader.load()

# Access the content
for doc in documents:
    print(doc.page_content)  # Prints the extracted text
    print(doc.metadata)      # Prints metadata including source file

Lazy Loading

If you're working with large CoNLL-U files and want to load documents one at a time to conserve memory:

# Using lazy loading
loader = CoNLLULoader("path/to/large/file.conllu")

for doc in loader.lazy_load():
    # Process each document individually
    print(doc.page_content)

Async Loading

For applications requiring asynchronous loading:

import asyncio
from langchain_community.document_loaders import CoNLLULoader

async def load_documents():
    loader = CoNLLULoader("path/to/your/file.conllu")
    
    # Load all documents asynchronously
    documents = await loader.aload()
    
    # Or load them one by one
    async for doc in loader.alazy_load():
        print(doc.page_content)

# Run the async function
asyncio.run(load_documents())

Loading and Splitting Documents

If you need to split the loaded documents into smaller chunks:

from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import CoNLLULoader

loader = CoNLLULoader("path/to/your/file.conllu")

# Create a text splitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

# Load and split the documents
split_docs = loader.load_and_split(text_splitter=text_splitter)

for doc in split_docs:
    print("Chunk:", doc.page_content)

Working with CoNLL-U Format

CoNLL-U files typically contain linguistic annotations in a specific format. Here's what a sample CoNLL-U file might look like:

# sent_id = 1
# text = They buy and sell books.
1   They    they    PRON    PRP    Case=Nom|Number=Plur    2   nsubj   _   _
2   buy     buy     VERB    VBP    Number=Plur|Person=3    0   root    _   _
3   and     and     CONJ    CC     _                       4   cc      _   _
4   sell    sell    VERB    VBP    Number=Plur|Person=3    2   conj    _   _
5   books   book    NOUN    NNS    Number=Plur             4   obj     _   _
6   .       .       PUNCT   .      _                       2   punct   _   _

The CoNLLULoader will extract the text content while maintaining sentence boundaries, making it useful for NLP tasks that require access to the raw text while preserving the original document structure.

Remember that while the loader extracts the text content, it doesn't preserve the linguistic annotations. If you need access to the detailed linguistic information, you might want to use a specialized CoNLL-U parsing library alongside LangChain.