Word sense disambiguation (WSD)

 

Word Sense Disambiguation in Natural Language Processing

Word sense disambiguation (WSD)  in Natural Language Processing (NLP) is the problem of identifying which “sense” (meaning) of a word is activated by the use of the word in a particular context or scenario. In people, this appears to be a largely unconscious process. The challenge of correctly identifying words in NLP systems is common, and determining the specific usage of a word in a sentence has many applications. The application of Word Sense Disambiguation involves the area of Information Retrieval, Question Answering systems, Chat-bots, etc.

Word Sense Disambiguation (WSD) is a subtask of Natural Language Processing that deals with the problem of identifying the correct sense of a word in context. Many words in natural language have multiple meanings, and WSD aims to disambiguate the correct sense of a word in a particular context. For example, the word “bank” can have different meanings in the sentences “I deposited money in the bank” and “The boat went down the river bank”.

WSD is a challenging task because it requires understanding the context in which the word is used and the different senses in which the word can be used. Some common approaches to WSD include:

1. Supervised learning: This involves training a machine learning model on a dataset of annotated examples, where each example contains a target word and its sense in a particular context. The model then learns to predict the correct sense of the target word in new contexts.
2. Unsupervised learning: This involves clustering words that appear in similar contexts together, and then assigning senses to the resulting clusters. This approach does not require annotated data, but it is less accurate than supervised learning.
3. Knowledge-based: This involves using a knowledge base, such as a dictionary or ontology, to map words to their different senses. This approach relies on the availability and accuracy of the knowledge base.
4. Hybrid: This involves combining multiple approaches, such as supervised and knowledge-based methods, to improve accuracy.

WSD has many practical applications, including machine translation, information retrieval, and text-to-speech systems. Improvements in WSD can lead to more accurate and efficient natural language processing systems.