Beginner Explanation
Imagine you have a friend who loves to play ‘bank’ in the river. If you hear them talking about ‘bank’, you might think they’re talking about a place where you keep money. But if you see them by the river, you know they mean the side of the river. Word sense disambiguation is like figuring out which meaning of ‘bank’ your friend is using based on where they are and what they’re talking about. It helps computers understand words in the right way, just like we do in conversations.Technical Explanation
Word Sense Disambiguation (WSD) is a natural language processing task aimed at determining which meaning of a word is intended in a given context. One common approach is to use supervised learning with labeled datasets. For example, using the WordNet database, we can train models to classify words based on their context. Here’s a simple Python example using the NLTK library: “`python import nltk from nltk.corpus import wordnet as wn nltk.download(‘wordnet’) # Function to get the most common sense of a word based on context def wsd(word, context): synsets = wn.synsets(word) # Here you would typically implement a method to score synsets based on context # For simplicity, we’ll just return the first synset return synsets[0].definition() # Example usage word = ‘bank’ context = ‘I went to the bank to deposit money.’ print(wsd(word, context)) “` This code demonstrates a basic approach, but more sophisticated models like BERT can leverage deep learning to better capture context and improve disambiguation accuracy.Academic Context
Word Sense Disambiguation (WSD) is a crucial task in computational linguistics and AI, primarily focused on resolving ambiguities in natural language. The mathematical foundation of WSD often involves probabilistic models, such as Hidden Markov Models (HMMs) and Bayesian networks, which are used to infer the most likely sense of a word given its context. Key papers in this field include ‘An Empirical Evaluation of Supervised Word Sense Disambiguation’ by Ng and Lee (1996), which compares various supervised learning techniques, and ‘A Survey on Word Sense Disambiguation’ by Navigli (2009), which provides a comprehensive overview of WSD methodologies. Recent advancements involve neural network models, such as BERT, which utilize contextual embeddings to enhance disambiguation accuracy.Code Examples
Example 1:
import nltk
from nltk.corpus import wordnet as wn
nltk.download('wordnet')
# Function to get the most common sense of a word based on context
def wsd(word, context):
synsets = wn.synsets(word)
# Here you would typically implement a method to score synsets based on context
# For simplicity, we’ll just return the first synset
return synsets[0].definition()
# Example usage
word = 'bank'
context = 'I went to the bank to deposit money.'
print(wsd(word, context))Example 2:
synsets = wn.synsets(word)
# Here you would typically implement a method to score synsets based on context
# For simplicity, we’ll just return the first synset
return synsets[0].definition()Example 3:
import nltk
from nltk.corpus import wordnet as wn
nltk.download('wordnet')
Example 4:
from nltk.corpus import wordnet as wn
nltk.download('wordnet')
# Function to get the most common sense of a word based on contextExample 5:
def wsd(word, context):
synsets = wn.synsets(word)
# Here you would typically implement a method to score synsets based on context
# For simplicity, we’ll just return the first synset
return synsets[0].definition()View Source: https://arxiv.org/abs/2511.16577v1