"Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context."
The process of identifying the intended meaning of a word from its context and multiple possible meanings.
Semantics: The study of meaning in language.
Word sense disambiguation (WSD): The process of identifying the correct meaning or sense of a word in context.
Lexical semantics: The study of word meanings and relationships between words.
Sense inventory: A list of possible senses or meanings for a particular word.
WordNet: A large lexical database that organizes words based on their semantic relationships.
Part-of-speech (POS) tagging: The process of identifying the grammatical category of a word (e.g. noun, verb, adjective).
Contextual features: Linguistic or non-linguistic cues in a text that can help identify the correct sense of a word.
Supervised learning: A machine learning approach that requires a labeled dataset to train a model.
Unsupervised learning: A machine learning approach that does not require labeled data and instead extracts patterns and structures from unlabeled data.
Neural networks: A machine learning approach that uses a network of interconnected nodes to process information.
Distributional semantics: The study of word meaning based on the distribution of words in language use.
Vector space models: A distributional semantics approach that represents words as vectors in a high-dimensional space based on their distribution in language use.
Ensemble methods: A machine learning approach that combines multiple models or algorithms to improve performance.
Evaluation metrics: Measures used to evaluate the accuracy and effectiveness of WSD systems, such as precision, recall, and F-score.
Lexical ambiguity: When a word has multiple meanings.
Syntactic ambiguity: When a sentence can be parsed in multiple ways due to the structure of the sentence.
Structural ambiguity: When a sentence can be interpreted in multiple ways due to the structure of the sentence.
Semantic ambiguity: When a sentence can have multiple interpretations because of the meaning of the words used in the sentence.
Pragmatic ambiguity: When a sentence can be interpreted in multiple ways due to the context of the sentence.
Irony: When a sentence is intended to convey a meaning opposite to its literal meaning.
Metaphor: When words are used in a non-literal way to convey an abstract concept.
Homonymy: When two words have the same spelling and pronunciation but different meanings.
Polysemy: When a word has multiple related meanings.
Word sense induction: When the context of a word is used to identify its meaning.
Word sense disambiguation: When the meaning of a word is identified based on the context in which it is used.
Supervised approach: When a machine learning technique is used to train a model to understand word sense disambiguation.
Unsupervised approach: When a machine learning technique is used to identify patterns in the data to understand word sense disambiguation.
Semi-supervised approach: When a combination of supervised and unsupervised techniques is used to understand word sense disambiguation.
Knowledge-based approach: When a database of domain-specific knowledge is used to understand word sense disambiguation.
"...ambiguity impairs clarity of communication..."
"...it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference."
"...developing the ability in computers to do natural language processing and machine learning."
"Many techniques have been researched, including dictionary-based methods, supervised machine learning methods, and completely unsupervised methods."
"Among these, supervised learning approaches have been the most successful algorithms to date."
"In English, accuracy at the coarse-grained (homograph) level is routinely above 90%..."
"The baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively."
"The process [WSD] aims to identify which meaning of a word (synset) is used in a given context."
"...important in numerous natural language processing tasks and applications such as machine translation, information retrieval, and sentiment analysis."
"In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication."
"The pervasive polysemy in natural language... affects the accuracy of sense disambiguation."
"...improving relevance of search engines..."
"...anaphora resolution, coherence, and inference."
"...a classifier is trained for each distinct word on a corpus of manually sense-annotated examples."
"...completely unsupervised methods that cluster occurrences of words, thereby inducing word senses."
"Top accuracies from 59.1% to 69.0% have been reported in evaluation exercises (SemEval-2007, Senseval-2)..."
"...it is an open problem that affects other computer-related writing..."
"...use the knowledge encoded in lexical resources."
"Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks..."