Babelnet thesaurus1/8/2024 In the 1970s, WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence, starting with Wilks' preference semantics. Later, Bar-Hillel (1960) argued that WSD could not be solved by "electronic computer" because of the need in general to model all world knowledge. Warren Weaver first introduced the problem in a computational context in his 1949 memorandum on translation. WSD was first formulated into as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics. "All words" task is generally considered a more realistic form of evaluation, but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement, rather than once for a block of instances for the same target word. WSD task has two variants: "lexical sample" (disambiguating the occurrences of a small sample of target words which were previously selected) and "all words" task (disambiguation of all the words in a running text). 11 External links and suggested readingĭisambiguation requires two strict inputs: a dictionary to specify the senses which are to be disambiguated and a corpus of language data to be disambiguated (in some methods, a training corpus of language examples is also required).4.1 Dictionary- and knowledge-based methods.3.5 Sense inventory and algorithms' task-dependency.On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively. In English, accuracy at the coarse-grained ( homograph) level is routinely above 90% (as of 2009), with some methods on particular homographs achieving over 96%. Among these, supervised learning approaches have been the most successful algorithms to date.Īccuracy of current algorithms is difficult to state without a host of caveats. Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, and completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks, computer science has had a long-term challenge in developing the ability in computers to do natural language processing and machine learning. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference. In human language prosessing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. Word-sense disambiguation ( WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. For other uses, see Disambiguation (disambiguation). For information on disambiguation of topic names in Wikipedia, see Wikipedia:Disambiguation.
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