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- Sequence_labeling abstract "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.".
- Sequence_labeling wikiPageExternalLink erdogan_icmla2010_tutorial_new.pdf.
- Sequence_labeling wikiPageID "29288159".
- Sequence_labeling wikiPageLength "3649".
- Sequence_labeling wikiPageOutDegree "21".
- Sequence_labeling wikiPageRevisionID "686483226".
- Sequence_labeling wikiPageWikiLink Artificial_intelligence.
- Sequence_labeling wikiPageWikiLink Bayesian_network.
- Sequence_labeling wikiPageWikiLink Categorical_variable.
- Sequence_labeling wikiPageWikiLink Category:Machine_learning.
- Sequence_labeling wikiPageWikiLink Conditional_random_field.
- Sequence_labeling wikiPageWikiLink Determiner.
- Sequence_labeling wikiPageWikiLink Hidden_Markov_model.
- Sequence_labeling wikiPageWikiLink Linear_dynamical_system.
- Sequence_labeling wikiPageWikiLink Machine_learning.
- Sequence_labeling wikiPageWikiLink Markov_chain.
- Sequence_labeling wikiPageWikiLink Maximum-entropy_Markov_model.
- Sequence_labeling wikiPageWikiLink Part-of-speech_tagging.
- Sequence_labeling wikiPageWikiLink Part_of_speech.
- Sequence_labeling wikiPageWikiLink Pattern_recognition.
- Sequence_labeling wikiPageWikiLink Probability_theory.
- Sequence_labeling wikiPageWikiLink Sequential_pattern_mining.
- Sequence_labeling wikiPageWikiLink Statistical_classification.
- Sequence_labeling wikiPageWikiLink Statistical_inference.
- Sequence_labeling wikiPageWikiLinkText "Sequence labeling".
- Sequence_labeling wikiPageWikiLinkText "labeling".
- Sequence_labeling wikiPageWikiLinkText "sequence labeling".
- Sequence_labeling wikiPageUsesTemplate Template:Empty_section.
- Sequence_labeling wikiPageUsesTemplate Template:Reflist.
- Sequence_labeling wikiPageUsesTemplate Template:Unreferenced.
- Sequence_labeling subject Category:Machine_learning.
- Sequence_labeling hypernym Task.
- Sequence_labeling type Agent.
- Sequence_labeling comment "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.".
- Sequence_labeling label "Sequence labeling".
- Sequence_labeling sameAs Q7452468.
- Sequence_labeling sameAs m.0ds4dv7.
- Sequence_labeling sameAs Q7452468.
- Sequence_labeling wasDerivedFrom Sequence_labeling?oldid=686483226.
- Sequence_labeling isPrimaryTopicOf Sequence_labeling.