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For example, a word can have several word senses. [3] Polysemy is distinct from monosemy, where a word has a single meaning. [3] Polysemy is distinct from homonymy—or homophony—which is an accidental similarity between two or more words (such as bear the animal, and the verb bear); whereas homonymy is a mere linguistic coincidence, polysemy ...
Or what everyday life was like for people living 50, 100, or more years ago. There’s an online community dedicated to sharing photos, scanned documents, articles, and personal anecdotes from the ...
In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.
A protester holds up a large black power raised fist in the middle of the crowd that gathered at Columbus Circle in New York City for a Black Lives Matter Protest spurred by the death of George Floyd.
For instance, the English word "row" can denote the action of rowing or to an arrangement of objects. In practice, polysemy and homonymy can be difficult to distinguish. [4] Phrases and sentences can also be semantically ambiguous, particularly when there are multiple ways of semantically combining its subparts. [5]
Pages in category "Polysemy" The following 8 pages are in this category, out of 8 total. This list may not reflect recent changes. ...
The rule of similarity states that images that are similar to each other can be grouped together as being the same type of object or part of the same object. Therefore, the more similar two images or objects are, the more likely it will be that they can be grouped together. For example, two squares among many circles will be grouped together.
Most people can agree in distinctions at the coarse-grained homograph level (e.g., pen as writing instrument or enclosure), but go down one level to fine-grained polysemy, and disagreements arise. For example, in Senseval-2, which used fine-grained sense distinctions, human annotators agreed in only 85% of word occurrences. [14]