We presented an improved probabilistic model for canonicalizing named entities into a table.

Here, we use a probabilistic model to infer a struc-

These choices highlight that the design of a probabilistic model can draw from both Bayesian and discriminative tools.

Our model is focused on the problem of canonicalizing mention strings into their parts, though its 7“ variables (which map mentions to rows) could be interpreted as (within-document and cross-document) coreference resolution, which has been tackled using a range of probabilistic models (Li et al., 2004; Haghighi and Klein, 2007; Poon and Domingos, 2008; Singh et al., 2011).

In this work, deterministic constraints are decoded before the application of probabilistic models , therefore lookahead features are made available during Viterbi decoding.

Since these deterministic constraints are applied before the decoding of probabilistic models , reliably high precision of their predictions is crucial.

However, when tagset BMES is used, the learned constraints don’t always make reliable predictions, and the overall precision is not high enough to constrain a probabilistic model .

Since different derivations may produce the same parse tree, recent work on TSG induction (Post and Gildea, 2009; Cohn et al., 2010) employs a probabilistic model of a TSG and predicts derivations from observed parse trees in an unsupervised way.

3.1 Probabilistic Model

We define a probabilistic model of an SR-TSG based on the Pitman-Yor Process (PYP) (Pitman and Yor, 1997), namely a sort of nonparametric Bayesian model.

Therefore, apart from the acoustic and language models used in conventional ASR, HVR also combines the haptic model as well as the PLI model to yield an integrated probabilistic model .

Therefore, fl, 5, and 7:1 can be obtained from the respective probabilistic models .

This framework allows coherent probabilistic models of different knowledge sources to be tightly integrated.