"Word sense disambiguation is a crucial task in NLP as it helps determine the intended meaning of a word with multiple senses in a given context. Choosing the right method depends on various factors like the availability of labeled data, computational resources, and the desired level of accuracy and interpretability. Often, a combination of approaches can be used to achieve the best results."- Gemini 2024

The word "bank" can have multiple meanings: a financial institution or the edge of a river. Determine the correct meaning in the following sentence:

• Dictionary and knowledge-based methods
  • These methods rely on external resources like dictionaries and lexical knowledge bases.
  • The Lesk algorithm is a classic example, where the sense of a word is chosen based on the overlap between its definition and the surrounding words' definitions in the dictionary.
  • While simple and efficient, these methods might not capture all the nuances of language and may be limited by the quality and coverage of the external resources.
• Supervised methods [Learn More]
  • These methods leverage machine learning algorithms trained on pre-annotated corpora where each word has its sense labeled.
  • Support Vector Machines (SVMs) and memory-based learning are popular choices due to their ability to handle high-dimensional feature spaces.
  • These methods require labeled data, which can be expensive and time-consuming to create.
• Unsupervised methods
  • These methods don't rely on pre-labeled data and try to identify word senses based on the context itself.
  • Clustering techniques can be used to group words based on their co-occurrence patterns, suggesting similar meanings.
  • While unsupervised methods can be appealing due to not requiring labeled data, they might struggle with ambiguity and may not always achieve the best accuracy.
• Embedding-based methods [Learn More]
  • This is a growing area that utilizes word embeddings, which are dense vector representations of words capturing semantic relationships.
  • The idea is that words with similar meanings will have similar vector representations in this high-dimensional space.
  • These methods can be powerful, but they often rely on pre-trained word embeddings and might not be easily interpretable.

import nltk
from nltk.corpus import wordnet

def lesk(sentence, word):
    word_senses = wordnet.synsets(word)
    best_sense = None
    max_overlap = 0

    for sense in word_senses:
        sense_definition = sense.definition()
        overlap = len(set(sentence.split()).intersection(
            set(sense_definition.split()))
        )
        if overlap > max_overlap:
            max_overlap = overlap
            best_sense = sense

    return best_sense

sentence = "I went to the bank to deposit money."
word = "bank"
sense = lesk(sentence, word)
print(sense)

# > Synset('bank.n.07')
    

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

# Sample data (trivial example)
data = {
    'sentence': [
        'I went to the bank to deposit money.',
        'The river bank is beautiful.',
        'The bank loan was approved.'
    ],
    'sense': [
        'financial institution',
        'river edge',
        'financial institution'
    ]
}

df = pd.DataFrame(data)

# Preprocess data
X = df['sentence']
y = df['sense']

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(X)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the classifier
clf = MultinomialNB()
clf.fit(X_train, y_train)

# Predict the sense for a new sentence
new_sentence = "I need to go to the bank to withdraw cash."
new_sentence_vec = vectorizer.transform([new_sentence])
predicted_sense = clf.predict(new_sentence_vec)[0]
print(predicted_sense)

# > financial institution
    

• Combine multiple techniques for better accuracy.
• Explore advanced techniques like supervised learning with deep neural networks.
• A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation (2024)