Please enable JavaScript.

Coggle requires JavaScript to display documents.

Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub…

- - - - Second, we exploit sublabel transitions using features {1(si−k ∈ P(yi−k)). . . 1(si ∈ P(yi))| ∀si−k, . . . , si ∈ S , ∀k ∈ 1 . . . m} .
      - Subsequently, instead of defining only (2), we additionally associate the feature functions X with all sub-labels s ∈ S by defining {χj (x, i)1(s ∈ P(yi))| ∀j ∈ 1 . . . |X | , ∀s ∈ S}
      - We begin by defining a function P(yi) which partitions any label yi into its sub-label components and returns them in an unordered set. For example, we could define P(PRON+1+SG) = {PRON, 1, SG}.
      - However, it does to some extent overlook some beneficial dependency information in case the labels have a rich sub-structure.
      - The baseline feature set described above can yield a high tagging accuracy given a conveniently simple label set, exemplified by the tagging results of Collins (2002) on the Penn Treebank (Marcus et al., 1993).
      - In what follows, we describe expanded feature sets which explicitly model the sub-label dependencies.
      - Note that we define the sub-label transitions up to order m, 1 ≤ m ≤ n, that is, an nth-order CRF model is not obliged to utilize sub-label transitions all the way up to order n.
      - This is because employing high-order sub-label transitions may potentially cause overfitting to training data due to substantially increased number of features (equivalent to the number of model parameters, |w| = |φ|).
      - For example, in a second-order (n = 2) model, it might be beneficial to employ the sub-label emission feature set (5) and first-order sub-label transitions while discarding second-order sub-label transitions.