achieving 97.36% on a standard benchmark dataset. Issues While there is broad agreement about basic categories, several edge cases make it difficult to settle on a single "correct" set of tags, even in a particular language such as (say) English. For example, it is hard to say whether "fire" is an adjective or a noun in the big green fire truck A second important example is the use/mention distinction, as in the following example, where "blue" could be replaced by a word from any POS (the Brown Corpus tag set appends the suffix "-NC" in such cases): the word "blue" has 4 letters. Words in a language other than that of the "main" text are commonly tagged as "foreign". In the Brown Corpus this tag (-FW) is applied in addition to a tag for the role the foreign word is playing in context; some other corpora merely tag such cases as "foreign", which is slightly easier but much less useful for later syntactic analysis. There are also many cases where POS categories and "words" do not map one to one, for example: as far as David's gonna don't vice versa first-cut cannot and/or pre- and post-secondary look (a word) up In the last example, "look" and "up" combine to function as a single verbal unit, despite the possibility of other words coming between them. Some tag sets (such as Penn) break hyphenated words, contractions, and possessives into separate tokens, thus avoiding some but far from all such problems. Many tag sets treat words such as "be", "have", and "do" as categories in their own right (as in the Brown Corpus), while a few treat them all as simply verbs (for example, the LOB Corpus and the Penn Treebank). These particular words have more forms than other English verbs, and occur in quite distinct grammatical contexts. As a POS tagger is being trained, collapsing them all into a single category "verb", makes it much harder to make use of those distinctions; depending on the particular methods being used, this can be a serious problem. For example, an HMM-based tagger, would only learn the overall probabilities for how "verbs" occur near other parts of speech, rather than learning distinct co-occurrence probabilities for "do", "have", "be", and other verbs. These English words have quite different distributions: one cannot just substitute other verbs into the same places where they occur; English grammar uses "have