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Sequence labeling — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Sequence_labeling
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-06T01:03:29.773Z
SHA-256 of the archived text: 943f44894416b67e00bc7aa8a6c1b777920fe68035b58b324fd485b861135418
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Sequence labeling — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Sequence_labeling (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_9769986abbc8 (Metraipolis archive dc_9769986abbc8, sha256 943f44894416b67e00bc7aa8a6c1b777920fe68035b58b324fd485b861135418)

BibTeX

@misc{dc9769986abbc8,
  title = {Sequence labeling — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Sequence_labeling},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_9769986abbc8; sha256 943f44894416b67e00bc7aa8a6c1b777920fe68035b58b324fd485b861135418},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Sequence_labeling. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. A common example of a sequence labeling task is part of speech tagging, which seeks to assign a part of speech to each word in an input sentence or document. Sequence labeling can be treated as a set of independent classification tasks, one per member of the sequence. However, accuracy is generally improved by making the optimal label for a given element dependent on the choices of nearby elements, using special algorithms to choose the globally best set of labels for the entire sequence at once. As an example of why finding the globally best label sequence might produce better results than labeling one item at a time, consider the part-of-speech tagging task just described. Frequently, many words are members of multiple parts of speech, and the correct label of such a word can often be deduced from the correct label of the word to the immediate left or right. For example, the word "sets" can be either a noun or verb. In a phrase like "he sets the books down", the word "he" is unambiguously a pronoun, and "the" unambiguously a determiner, and using either of these labels, "sets" can be deduced to be a verb, since nouns very rarely follow pronouns and are less likely to precede determiners than verbs are. But in other cases, only one of the adjacent words is similarly helpful. In "he sets and then knocks over the table", only the word "he" to the left is helpful (cf. "...picks up the sets and then knocks over..."). Conversely, in "... and also sets the table" only the word "the" to the right is helpful (cf. "... and also sets of books were ..."). An algorithm that proceeds from left to right, labeling one word at a time, can only use the tags of left-adjacent words and might fail in the second example above; vice versa for an algorithm that proceeds from right to left. Most sequence labeling algorithms are probabilistic in nature, relying on statistical inference to find the best sequence. The most common statistical models in use for sequence labeling make a Markov assumption, i.e. that the choice of label for a particular word is directly dependent only on the immediately adjacent labels; hence the set of labels forms a Markov chain. This leads naturally to the hidden Markov model (HMM), one of the most common statistical models used for sequence labeling. Other common models in use are the maximum entropy Markov model and conditional random field.

Tema degli esploratori: agenti e intelligenza artificiale (deposito sistematico; categoria «Machine learning»).
Fonte: Wikipedia in inglese, voce «Sequence labeling» — https://en.wikipedia.org/wiki/Sequence_labeling
Wikidata Q7452468 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q7452468
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Sequence_labeling?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1297371070, consultata il 2026-10-06T00:21:08.896Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 9457cc68361d236156de3fbe8fe7a3b5c88f6608901b8c9466c1b58b98b93cb9