Overview of the Lesk Algorithm and Related Algorithms and Examples of Implementations

Overview of Lesk Algorithm

The Lesk algorithm is one of the methods used in the field of natural language processing to determine the meaning of words, and in particular, it is an approach used for Word Sense Disambiguation (WSD). Word sense disambiguation is the problem of selecting the correct sense for a word when it has several different senses, depending on the context.

The outline of the Lesk algorithm is as follows:

1. use of dictionary definitions:

The Lesk algorithm makes use of dictionaries and thesauruses such as WordNet. Each word has multiple meanings registered as a dictionary definition.

2. collection of words around a context word:

Context words around the target word are collected. Usually, several words before and after the word and co-occurrences within a sentence are taken into account.

3 Calculate the degree of overlap of each meaning with the dictionary definition:

For each meaning of the target word, the degree of overlap between the dictionary definition of the meaning and the context word is calculated. The degree of overlap is based on the number of shared words, word importance, etc.

4. selecting the meaning with the most overlap:

The meaning with the highest overlap (most shared words) is selected for interpretation by the Lesk algorithm.

The Lesk algorithm is a relatively simple yet effective method for determining the meaning of a word in context, but its performance may be limited when the number of dictionary definitions for a particular word is large or the complexity of the context is high. Therefore, more advanced and complex methods have been proposed.

Lesk Algorithm Procedure

The Lesk algorithm follows the procedure described below. This is the basic Lesk algorithm procedure for Word Sense Disambiguation (WSD) of a particular word.

1. Collection of dictionary definitions of the target word: The Lesk algorithm collects dictionary definitions of the target word. This is obtained from a dictionary or thesaurus such as WordNet.
2. Context collection: The context containing the target word is collected. Usually, several words before and after the target word and co-occurrence relations within a sentence are taken into account.
3. Comparison of each dictionary definition with its context: For each dictionary definition, the degree of overlap between its meaning and the words in the context is calculated. This is calculated using the number of shared words and the importance of the words.
4. Selection of the dictionary definition with the most overlap: The dictionary definition with the highest overlap (most shared words) is selected for interpretation by the Lesk algorithm.

The following is the procedure of the Lesk algorithm with a simple pseudo code.

function Lesk(word, context):
    definitions = get_definitions_from_dict(word)  # Collection of dictionary definitions
    best_sense = None
    max_overlap = 0
    
    for sense in definitions:
        overlap = compute_overlap(sense, context)  # Calculating the degree of overlap with the context
        if overlap > max_overlap:
            max_overlap = overlap
            best_sense = sense
    
    return best_sense

In this pseudo code, get_definitions_from_dict(word) is a function to get the dictionary definition of a word, and compute_overlap(sense, context) is a function to calculate the degree of overlap between the dictionary definition and context, which selects the dictionary definition with the most overlap and performs polysemy resolution using Lesk algorithm.

Application of the Lesk Algorithm

The following are examples of the application of the Lesk algorithm.

1. machine translation: The Lesk algorithm is used to accurately capture the meaning of words during machine translation. Proper interpretation of words with multiple meanings contributes to the quality of translation.

2. Information Retrieval: The Lesk algorithm is also useful in information retrieval, improving the relevance of search results by eliminating word ambiguity in search queries and documents.

3. question answering systems: Question answering systems may use the Lesk algorithm to resolve polysemous words in a question in order to accurately answer the user’s question.

4. document classification: In text classification and document classification tasks, there is a need to accurately capture the meaning of words according to context, and the Lesk algorithm is used in these areas.

5. Information Extraction: The Lesk algorithm is also useful in information extraction, where the correct interpretation of words with specific meanings improves the accuracy of the extracted information.

Natural Language Generation: In the task of natural language generation, it is important that the generated sentences are semantically appropriate, and the Lesk algorithm is used to accurately determine the meaning of the words to be generated.

Examples of Implementations of the Lesk Algorithm for Information Retrieval

When the Lesk algorithm is implemented for information retrieval, it is usually used to resolve word polysemy in search queries and documents. The following is a simple example of applying the Lesk algorithm to information retrieval. This example uses Python and the NLTK (Natural Language Toolkit) library.

First, install NLTK.

pip install nltk

Next, create a Python script to implement the Lesk algorithm.

from nltk.wsd import lesk
from nltk.tokenize import word_tokenize

# Function to perform polysemy resolution using NLTK's Lesk algorithm
def perform_lesk_disambiguation(sentence, ambiguous_word):
    # Tokenize statement
    tokens = word_tokenize(sentence)
    
    # Polysemy resolution by Lesk algorithm
    sense = lesk(tokens, ambiguous_word)
    
    return sense

# Examples of search queries and documents
query = "I saw a bat in the zoo."
document = "The baseball player hit the bat with the ball. The bat flew away."

# Select "bat" as a polysemous word
ambiguous_word = "bat"

# Perform polysemy resolution using Lesk algorithm
sense = perform_lesk_disambiguation(query, ambiguous_word)

# Display Results
print(f"Original Sentence: {query}")
print(f"Ambiguous Word: {ambiguous_word}")
print(f"Chosen Sense: {sense.definition()}")

In this example, NLTK’s Lesk algorithm is used to perform polysemy resolution for “bat”. The context in the search query is considered and the meaning most appropriate for the context is selected.

Challenges of the Lesk Algorithm and Measures to Address Them

The Lesk algorithm is a useful polysemy resolution method, but several challenges exist. The following are the main challenges of the Lesk algorithm and general measures to deal with them.

1. contextual restrictiveness:

Challenge: The Lesk algorithm can only consider context locally, making it difficult to grasp the broader context of a sentence.
Solution: To obtain broader context, the method of obtaining context could be improved, for example, by considering the entire surrounding context.

2. lack of dictionaries:

Challenge: The dictionaries and thesauruses used may not adequately cover all meanings and usages of words.
Solution: Consider expanding dictionaries by using larger, more comprehensive dictionaries and thesauruses, building domain-specific dictionaries, etc.

3. increasing polysemy:

Challenge: If a particular word has polysemy, the performance of the Lesk algorithm may degrade.
Solution: Consider introducing more advanced methods or machine learning approaches that can capture complex contexts.

4. terminology processing:

Challenge: The Lesk algorithm is effective for common words, but may not be able to handle technical terms or new terms.
Solution: For jargon and new terms, domain-specific dictionaries or specialized knowledge bases could be used.

5. impact of stemming and lemmatization:

Challenge: Pre-processing such as stemming and lemmatization may change word morphology and affect the performance of the Lesk algorithm.
Solution: When stemming or lemmatization is performed, consider a preprocessing method that preserves the stem information or retains the original morphology.

Reference Information and Reference Books

For more information on natural language processing in general, see “Natural Language Processing Technology” and “Overview of Natural Language Processing and Examples of Various Implementations.

Reference books include “Natural language processing (NLP): Unleashing the Power of Human Communication through Machine Intelligence“.

“Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems“

“Natural Language Processing With Transformers: Building Language Applications With Hugging Face“