Python | Gender Identification by name using NLTK

Python | Gender Identification by name using NLTK

The Natural Language Toolkit (NLTK) for Python provides a simple way to determine the likely gender of a name using the nltk.corpus.names corpus. This corpus contains lists of common male and female names.

Here's a basic method to identify the gender based on a name using NLTK:

  1. Installing and Importing Necessary Modules:

    First, make sure you have NLTK installed:

    pip install nltk

    Then, download the names dataset:

    import nltk nltk.download('names')

  2. Gender Identification Function:

    from nltk.corpus import names def gender_identification(name): name = name.lower() if name in names.words('male.txt'): return 'male' elif name in names.words('female.txt'): return 'female' else: return 'unknown' # Test print(gender_identification("John")) # Output: male print(gender_identification("Mary")) # Output: female print(gender_identification("Alex")) # Output: unknown (because Alex can be both male and female)

  3. Improving Efficiency:

    The above approach can be inefficient for multiple look-ups because it reads the name lists every time the function is called. To improve this, you can cache the lists:

    male_names = set(names.words('male.txt')) female_names = set(names.words('female.txt')) def gender_identification(name): name = name.lower() if name in male_names: return 'male' elif name in female_names: return 'female' else: return 'unknown'

Note: This method is based on a fixed list of names and may not be exhaustive or entirely accurate, especially for non-English names or unisex names. It's a heuristic approach, and there may be exceptions. If you need a more comprehensive solution, you might want to explore machine learning-based approaches trained on a larger dataset.

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