GMT may mean:

Greenwich Mean Time (GMT) is a time system originally referring to mean solar time at the Royal Observatory in Greenwich, London, which later became adopted as a global time standard. It is arguably the same as Coordinated Universal Time (UTC) and when this is viewed as a time zone the name Greenwich Mean Time is especially used by bodies connected with the United Kingdom, such as the BBC World Service, the Royal Navy, the Met Office and others.

Before the introduction of UTC on 1 January 1972 Greenwich Mean Time (also known as Zulu time) was the same as Universal Time (UT) which is a standard astronomical concept used in many technical fields. Astronomers no longer use the term "Greenwich Mean Time".

In the United Kingdom, GMT is the official time only during winter; during summer British Summer Time is used. GMT is the same as Western European Time.

Noon Greenwich Mean Time is rarely the exact moment when the sun crosses the Greenwich meridian (and reaches its highest point in the sky at Greenwich) because of Earth's uneven speed in its elliptic orbit and its axial tilt. This event may be up to 16 minutes away from noon GMT (a discrepancy calculated by the equation of time). The fictitious mean sun is the annual average of this nonuniform motion of the true Sun, necessitating the inclusion of mean in Greenwich Mean Time.

In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings (polysemy). The solution to this problem impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference et cetera.

Research has progressed steadily to the point where WSD systems achieve sufficiently high levels of accuracy on a variety of word types and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.