Editing Word-sense disambiguation (section)

===Local impediments and summary===
The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem. [[Unsupervised learning|Unsupervised methods]] rely on knowledge about word senses, which is only sparsely formulated in dictionaries and lexical databases. [[Supervised learning|Supervised methods]] depend crucially on the existence of manually annotated examples for every word sense, a requisite that can so far{{when|date=February 2019}} be met only for a handful of words for testing purposes, as it is done in the [[Senseval]] exercises.

One of the most promising trends in WSD research is using the largest [[Corpus linguistics|corpus]] ever accessible, the [[World Wide Web]], to acquire lexical information automatically.{{sfn|Kilgarrif|Grefenstette|2003|pp=333–347}} WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as [[information retrieval]] (IR). In this case, however, the reverse is also true: [[web search engine]]s implement simple and robust IR techniques that can successfully mine the Web for information to use in WSD. The historic lack of training data has provoked the appearance of some new algorithms and techniques, as described in [[Automatic acquisition of sense-tagged corpora]].