What is CoNLL Data Format?

The CoNLL data format, commonly used in computational linguistics and natural language processing (NLP), refers to a text format that facilitates the organization and processing of linguistic data for tasks such as part-of-speech tagging, syntactic parsing, and named entity recognition. Originally developed for use in the Conference on Natural Language Learning (CoNLL) shared tasks, this format has become a standard due to its simplicity and effectiveness in handling annotated linguistic data.

Understanding the CoNLL Format

The CoNLL data format typically consists of plain text files where each word or token in the text is separated by new lines and each sentence is separated by a blank line. A CoNLL file features columns where each column represents a specific type of linguistic information such as the word itself, its lemma, part-of-speech tag, and syntactic information.

Common Features of CoNLL Format:

• Columns: Each line in a CoNLL file contains several columns, each separated by a space or tab. These columns represent different features like word form, lemma, part-of-speech tag, and syntactic relations.
• Sentence Delimiters: Sentences are separated by blank lines, making it easy to distinguish between individual sentences in a text.
• Annotations: Annotations such as named entity labels or dependency parse information are included directly in the lines corresponding to each token or word.

Variants of CoNLL Format

There are several variants of the CoNLL format, each adapted for specific linguistic tasks:

• CoNLL-U: This extension is used by the Universal Dependencies project. It adds support for multiword tokens and complex morphological annotations.
• CoNLL-2003: Specifically designed for named entity recognition tasks, it includes columns for text tokens, part-of-speech tags, syntactic chunk tags, and named entity tags.
• CoNLL-X and CoNLL-2009: These formats are tailored for dependency parsing, with CoNLL-2009 supporting semantic role labeling layers as well.

Python Implementation to Read CoNLL File

Let's create a sample CoNLL-like file with annotations for word, lemma, part-of-speech (POS) tag, and a simple syntactic dependency label. For the purpose of this example, we will use a very basic format CoNLL-U format but simplified.

conll_u_data = """# sent_id = 1
# text = The quick brown fox jumps over the lazy dog.
1	The	the	DET	DT	Definite=Def|PronType=Art	5	det	_	_
2	quick	quick	ADJ	JJ	Degree=Pos	5	amod	_	_
3	brown	brown	ADJ	JJ	Degree=Pos	5	amod	_	_
4	fox	fox	NOUN	NN	Number=Sing	5	nsubj	_	_
5	jumps	jump	VERB	VBZ	Mood=Ind|Tense=Pres|VerbForm=Fin	0	root	_	_
6	over	over	ADP	IN	_	9	case	_	_
7	the	the	DET	DT	Definite=Def|PronType=Art	9	det	_	_
8	lazy	lazy	ADJ	JJ	Degree=Pos	9	amod	_	_
9	dog	dog	NOUN	NN	Number=Sing	5	nmod	_	_
10	.	.	PUNCT	.	_	5	punct	_	_
"""

# Write to file
file_path = "Sample_ConllU_File.conllu"
with open(file_path, "w") as file:
    file.write(conll_u_data)

file_path

Output:

'Sample_ConllU_File.conllu'

Here's a Python script to read the above CoNLL-formatted data:

1. Reading the File: The function read_conll_format reads a file line by line.
2. Structure Data: Each line (representing a token and its annotations) is split into parts and organized into a dictionary.
3. Sentence Handling: Sentences are separated by blank lines. Each sentence is stored as a list of dictionaries (one per token), and all sentences are aggregated into a list of such lists.
4. Output: The script outputs each token's data in a structured format for easy processing.

def read_conll_format(file_path):
    sentences = []
    current_sentence = []
    
    with open(file_path, 'r') as file:
        for line in file:
            if line.strip() == "":  # New sentence or end of file
                if current_sentence:
                    sentences.append(current_sentence)
                    current_sentence = []
            else:
                if line.startswith('#'):  # Skip comment lines
                    continue
                # Split the line into components
                parts = line.strip().split()
                token_data = {
                    'id': parts[0],
                    'form': parts[1],
                    'lemma': parts[2],
                    'pos': parts[3],
                    'head': parts[4],
                    'dep_rel': parts[5]
                }
                current_sentence.append(token_data)
        
        if current_sentence:  # Add the last sentence if the file doesn't end with a newline
            sentences.append(current_sentence)

    return sentences

# Assuming 'sample.conllu' is your CoNLL file
conll_data = read_conll_format('sample.conllu')
for sentence in conll_data:
    print("Sentence:")
    for token in sentence:
        print(token)

Output:

Sentence:
{'id': '1', 'form': 'The', 'lemma': 'the', 'pos': 'DET', 'head': 'DT', 'dep_rel': 'Definite=Def|PronType=Art'}
{'id': '2', 'form': 'quick', 'lemma': 'quick', 'pos': 'ADJ', 'head': 'JJ', 'dep_rel': 'Degree=Pos'}
{'id': '3', 'form': 'brown', 'lemma': 'brown', 'pos': 'ADJ', 'head': 'JJ', 'dep_rel': 'Degree=Pos'}
{'id': '4', 'form': 'fox', 'lemma': 'fox', 'pos': 'NOUN', 'head': 'NN', 'dep_rel': 'Number=Sing'}
{'id': '5', 'form': 'jumps', 'lemma': 'jump', 'pos': 'VERB', 'head': 'VBZ', 'dep_rel': 'Mood=Ind|Tense=Pres|VerbForm=Fin'}
{'id': '6', 'form': 'over', 'lemma': 'over', 'pos': 'ADP', 'head': 'IN', 'dep_rel': '_'}
{'id': '7', 'form': 'the', 'lemma': 'the', 'pos': 'DET', 'head': 'DT', 'dep_rel': 'Definite=Def|PronType=Art'}
{'id': '8', 'form': 'lazy', 'lemma': 'lazy', 'pos': 'ADJ', 'head': 'JJ', 'dep_rel': 'Degree=Pos'}
{'id': '9', 'form': 'dog', 'lemma': 'dog', 'pos': 'NOUN', 'head': 'NN', 'dep_rel': 'Number=Sing'}
{'id': '10', 'form': '.', 'lemma': '.', 'pos': 'PUNCT', 'head': '.', 'dep_rel': '_'}

The information you provided describes each word in a sentence using a structured format suitable for CoNLL-U, which is a standard for annotating text in a way that supports natural language processing tasks such as syntactic parsing.

Here is a breakdown of the sentence components based on the CoNLL-U format:

1. ID: Sequential identifier for each token (word or punctuation mark) in the sentence.
2. FORM: Actual form of the token as it appears in the sentence.
3. LEMMA: Base or dictionary form of the token.
4. POS (Part of Speech): Universal part-of-speech tag according to a standardized tagset.
5. HEAD: The identifier of the token that governs this token in a dependency parse of the sentence.
6. DEP_REL (Dependency Relation): Describes the type of syntactic relation that connects this token to its HEAD. In cases where this field is underscore (_), it suggests that a specific relation or feature is not applicable or unspecified in this context.

Applications of CoNLL Format

The CoNLL format is widely used in various NLP tasks due to its straightforward structure, which facilitates easy parsing and manipulation of textual data:

• Training Machine Learning Models: NLP models for tasks like named entity recognition and dependency parsing often use datasets in CoNLL format for training.
• Linguistic Research: Researchers use the CoNLL format to analyze linguistic features and build annotated linguistic datasets.
• Tool Development: Many NLP tools and libraries support the CoNLL format for input and output, which simplifies the development of applications requiring linguistic data processing.

Conclusion

The CoNLL data format is a cornerstone in the field of computational linguistics, providing a structured and flexible way to handle annotated linguistic data. Its wide adoption across different NLP tasks underscores its utility and effectiveness in facilitating linguistic research and the development of NLP applications. Whether you are a researcher, developer, or enthusiast, understanding and utilizing the CoNLL format can significantly enhance your NLP projects and workflows.