We present a novel approach for building verb subcategorization leXicons using a simple graphical model .

We discuss the advantages of graphical models for this task, in particular the ease of integrating semantic information about verbs and arguments in a principled fashion.

Our initial attempt at applying graphical models to subcategorization also suggested several ways to extend and improve the method.

In this section we describe the basic components of our study: feature sets, graphical model , inference, and evaluation.

Our graphical modeling approach uses the Bayesian network shown in Figure 1.

Graphical models have been increasingly popular for a variety of tasks such as distributional semantics (Blei et al., 2003) and unsupervised POS tagging (Finkel et al., 2007), and sampling methods allow efficient estimation of full joint distributions (Neal, 1993).

This is an example of how bad decisions made by the parser cannot be fixed by the graphical model , and an area where pGR features have an advantage.

For example, in (Stolcke et al., 2000), Hidden Markov Models (HMMs) were used for DA tagging; in (J i and Bilmes, 2005), different types of graphical models were explored.

Linear-chain CRFs is a type of undirected graphical models .

Distribution of a set of variables in undirected graphical models can be written as

CRFs is a special case of undirected graphical model in which w are log-linear functions:

For higher accuracy, a graphical model such as a conditional random field (CRF) is constructed from the compatibility functions to jointly reason about the pairwise decisions (McCallum and Wellner, 2004).

The pairwise compatibility functions become the factors in the graphical model .

Figure 2: Pairwise model on six mentions: Open circles are the binary coreference decision variables, shaded circles are the observed mentions, and the black boxes are the factors of the graphical model that encode the pairwise compatibility functions.

Techniques such as lifted inference (Singla and Domingos, 2008) for graphical models exploit redundancy in the data, but typically do not achieve any significant compression on coreference data be-

We propose a novel graphical model to simultaneously conduct N ER and N EN on multiple tweets to address these challenges.

We propose jointly conducting NER and NEN on multiple tweets using a graphical model , to address these challenges.

We adopt a factor graph as our graphical model , which is constructed in the following manner.

Bayesian logic programs (BLPs) (Kersting and De Raedt, 2007; Kersting and Raedt, 2008) can be considered as templates for constructing directed graphical models (Bayes nets).

Unlike BLPs, this approach does not use a well-founded probabilistic graphical model to compute coherent probabilities for inferred facts.

However, MLNs include all possible type-consistent groundings of the rules in the corresponding Markov net, which, for larger datasets, can result in an intractably large graphical model .